Rapid determination of spore germinability of Clostridium perfringens based on microscopic hyperspectral imaging technology and chemometrics

Journal Pre-proof Rapid determination of spore germinability of Clostridium perfringens based on microscopic hyperspectral imaging technology and chemometrics Yaodi Zhu, Jiaye Zhang, Miaoyun Li, Lijun Zhao, Hongrong Ren, Longgnag Yan, Gaiming Zhao, Chaozhi Zhu PII:

S0260-8774(19)30539-4

DOI:

https://doi.org/10.1016/j.jfoodeng.2019.109896

Reference:

JFOE 109896

To appear in:

Journal of Food Engineering

Received Date: 15 August 2019 Revised Date:

21 November 2019

Accepted Date: 26 December 2019

Please cite this article as: Zhu, Y., Zhang, J., Li, M., Zhao, L., Ren, H., Yan, L., Zhao, G., Zhu, C., Rapid determination of spore germinability of Clostridium perfringens based on microscopic hyperspectral imaging technology and chemometrics, Journal of Food Engineering (2020), doi: https://doi.org/10.1016/ j.jfoodeng.2019.109896. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

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Manuscript For Food Engineering

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Rapid Determination of Spore Germinability of Clostridium

4

perfringens based on Microscopic Hyperspectral Imaging

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Technology and Chemometrics

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Yaodi Zhu#1,2, Jiaye Zhang#1, Miaoyun Li*, Lijun Zhao1, Hongrong Ren1, Longgnag Yan1,

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Gaiming Zhao1,Chaozhi Zhu1

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1. College of Food Science and Technology, Henan Key Laboratory of Meat Processing and

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Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China

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2. Postdoctoral workstation of hengdu Food Co., LTD, Zhumadian 463700, PR China

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#.The authors contributed equally to this study.

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*Corresponding author. Tel.: 86-371-63558150; fax: 86-371-63558150.

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E-mail: [email protected] (Miaoyun Li)

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1

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Abstract

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The Gram-positive, anaerobic, spore-forming bacterium, Clostridium perfringens

17

(C. perfringens) causes a variety of diseases in humans and other animals. Spore

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germination is thought to be the first stage of infection by C. perfringens. AGFK, a

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mixture of L-asparagine, D-glucose, D-fructose, and potassium ions, is an effective

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nutrient germinant. The objective of this study was to investigate the effects of

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different AGFK concentrations (0, 50, 100, 200 mM/mL) on C. perfringens spore

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germination. This paper proposes a novel rapid method for the measurement of spore

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germinability based on microscopic hyperspectral imaging technology (HSIT). The

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spore germination rate (Srate), the OD600% and Ca2+-DPA% of C. perfringens were

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determined by chemical methods under different concentrations of AGFK. The results

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showed that spores have a maximum germination rate of 94.59% after 80 min with

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100 mM/mL AGFK. Microscopic HSIT revealed that the spectral and spatial

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characteristics of spores varied during the spore germination process. Multivariate

29

analyses (GA-siPLS and GA-PLS) and the gray symbiotic matrix (GLCM) were used

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to extract highly correlated spectral and spatial descriptors from the time-series data

31

from microscopic HSIT, respectively. Single spectral, spatial signals and data fusion

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of spectral and spatial information were then used to predict the Srate, the OD600% and

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Ca2+-DPA % by GA-PLS, respectively. The result show that the Srate calibration was

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built by GA-PLS using data fusion variables and yielded acceptable results (Rc = 0.96,

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RMSEC = 0.64, Rcv = 0.93, RMSEP = 0.87, Rp = 0.94). The OD600% optimal model

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was built by GA-PLS using image variables and yielded acceptable results (Rc = 0.93,

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RMSEC = 19.36, Rcv = 0.91, RMSEP = 24.36, Rp = 0.89). For Ca2+-DPA %, the model

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based on the fusion of spectral and imaging data was optimal. The Ca2+-DPA %

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calibration yielded acceptable results (Rc = 0.95, RMSEC = 49.83, Rcv = 0.93, RMSEP

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= 58.98, Rp = 0.92). This work demonstrates the potential of microscopic HSIT for the

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non-destructive detection of spore germinability. The data fusion models also

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significantly improved the prediction of spore germinability. In conclusion,

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microscopic HSIT exhibits considerable promise for nondestructive diagnostics of

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spore germination. 2

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Keywords: Clostridium perfringens, Spore germinability, AGFK, Microscopic

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hyperspectral imaging technology, Data fusion, chemometrics

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3

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1. Introduction

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Clostridium perfringens (C. perfringens) is a Gram-positive, anaerobic,

50

spore-forming pathogenic bacterium causing gastrointestinal (GI) diseases in humans

51

and animals (Grass and Gould et al., 2013). The spores of C. perfringens, which are

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extremely resistant to environmental stresses, such as heat, radiation, and toxic

53

chemicals, can survive food preservation processes and upon germination outgrowth

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to be converted into vegetative cells that can breed and produce enterotoxins, cause

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food spoilage and safety risks (Setlow, P., and E. A. Johnson, 2007, Monma and

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Hatakeyama et al., 2015). Thus, C. perfringens spores are important morphotypes for

57

infection (Grass et al., 2013; May et al., 2016).

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Spores in nature germinate only in response to nutrients, termed germinants

59

(Komatsu and Inui et al., 2012). Once spore germination, they lost the extreme

60

resistance of dormant spores and are thus relatively easy to kill (Komatsu and Inui et

61

al., 2012). A mixture of L-asparagine, D-glucose, D-fructose, and potassium ions

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(AGFK) is an effective nutrient germinant (Setlow, 2003). Within minutes or hours of

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mixing spores, different concentration of AGFK can cause the different effect on the

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spores germination (McClane, Robertson, & Li, 2013, Wang and Li, 2018). Spore

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germination rate (Srate), which is routinely calculated by plant count, is a very

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important significance to the meat industry and the research field on the prevention

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and control of food spoilage and foodborne disease of C. perfringens. The release of

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Ca2+-DPA (Ca2+-DPA%) varies with the duration of germination and can be used to

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explain the spore germination process. The completion of Ca2+-DPA release is a

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hallmark of the completion of spore germination (Kawarizadeh and Tabatabaei et al.,

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2019). The progress of a spore’s germination can be determined based on the

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transformation of spores from phase-bright to dark-phase on microscopic images.

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Changes in spore refractivity can be measured by phase-contrast microscopy due to

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the release and spore cortex hydrolysis of Ca2+-DPA (Setlow and Wang et al., 2017).

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Thus, changes in the brightness of time-series images provide a better understanding

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of the overall germination process. Besides, monitoring the optical density at 600 nm

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(OD600) of spore cultures, which drops 60% upon complete spore germination 4

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(Paredes-Sabja and Setlow et al., 2011). The relationship between OD600 and the level

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of spore germination was confirmed by phase-contrast microscopy. Thus, the Srate, the

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loss of C. perfringens spore refractivity (OD600%) and the release of Ca2+-DPA

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(Ca2+-DPA%) are key indicators of the spore germination and can be used to assess

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the rate of spore germination or the optimal concentration of germination agents.

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At present, various traditional physical and chemical methods are used to

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calculate Srate, OD600% and Ca2+-DPA % during spore germination (Rao and Feeherry

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et al., 2018). Such methods can achieve high accuracies, but they are tedious,

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expensive, and time-consuming, which makes them unsuitable for rapid assessments.

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Raman spectroscopy (Wang and Doona et al., 2016), near-infrared spectroscopy

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(NIRS) (Eady and Setia et al., 2019) and hyperspectral imaging technology (HSIT)

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(Tao and Peng et al., 2012) are rapid, noninvasive, and chemical-free techniques that

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have been widely developed for measurements of chemical data and microorganisms

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in the food industry. Such spectroscopic techniques have unique advantages, but

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remain limited due to the inherently weak scattering signals and the strong

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interference of biological fluorescence background in Raman spectroscopy, the

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limited “single spectrum” without spatial information in NIR analysis, and the low

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resolution for microorganisms in the HSI approach. Microscopic hyperspectral

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imaging technology (HSIT) is an emerging technique that integrates microscopic

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imaging and spectroscopy to obtain 2-D spatial and 1-D spectral information from

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analytes (Monma and Hatakeyama et al., 2015). In recent years, microscopic HSIT

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has become known as a promising method that integrates hyperspectral data with

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microscopic imaging, which has been successfully used to capture spectral and spatial

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information of tissue sections (Li and Xue et al., 2008, Gao and Smith, 2015).

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In this study, microscopic HSIT was used to rapidly predict Srate, OD600% and

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Ca2+-DPA % over time during C. perfringens spore germination under different

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AGFK concentrations. We found that spectral at wavelength regions of 484.2-610.4

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nm, 442.3-654.1 nm and 463.2-588.7 nm were significantly correlated with the Srate,

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OD600%, and Ca2+-DPA % of C. perfringens spore during germination process. In

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addition, microscopic hyperspectral images of spore had the potential of determine 5

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spore germination according color, texture and form during spore germination process.

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Therefore microscopic hyperspectral was used to predict the spore germinability

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under different concentration germinents to control C. perfringens. The objectives of

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this study were to: (1) measured the Srate, acquire time-lapse phase-contrast images to

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quantifying the loss of C. perfringens spore optical density (OD600%) and Ca2+-DPA%;

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(2) compare and analyze the effects different AGKF concentrations on Srate, OD600%,

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and Ca2+-DPA % during spore germination; (3) acquire microscopic hyperspectral

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images, and extract and preprocess spectral and image data; and (4) build the

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calibration models of Srate, as well as the OD600% and Ca2+-DPA% based on the single

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spectral, spatial and data fusion signals under different AGFK concentrations,

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respectively. The optimum model was then selected and verified.

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2. Materials and methods

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2.1 Preparation of strains and spores

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Wild-type

C.

perfringens

(strain

C1)

was

directly

isolated

from

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vacuum-packaged cooked meat by the Microbiology Laboratory of the College of

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Food Science and Technology at the He Nan Agricultural University (Zhengzhou,

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China), and identified by Sangon Biotech Co., Ltd. (Shanghai, China). Spores were

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prepared using our previously-described method (Juneja et al., 1993). The spore crop

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containing ~107 CFU/mL was stored at -80oC until use.

127

The spore crop was prepared separately from each strain of C. perfringens (~107

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CFU/mL). Briefly, an aliquot (0.1 ml) of stock culture was inoculated in 10 ml of

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freshly prepared fluid thioglycolate medium (FTG, Difco, Becton Dickinson, Sparks,

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MD). C. perfringens spores were heat shocked for 20 min at 75 ℃ in a

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submerged-coil water bath, then cooled in chilled water (4℃), and incubated for 18 h

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at 37℃. A 1.0 ml volume of the culture was transferred to 10 ml of freshly steamed

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FTG, followed by incubation for 24 h at 37℃. The fresh culture (1%) was then

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transferred to a modified sporulation broth and incubated aerobically for 24 h at 37℃.

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The presence of spores was confirmed by phase-contrast microscopy. The cultures of

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each strain were then concentrated by centrifugation at 7012 g for 20 min at 4℃

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(GS-15R, Beckman, Palo Alto, CA). Concentrated cultures were then washed twice 6

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with 50 ml of sterile distilled water until spore suspensions were > 99% free from cell

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debris, and sporulating and germinating cells, as determined by phase-contrast

140

microscopy. Spore suspensions were adjusted with sterile distilled water to a final

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optical density at 600 nm (OD600) ~6.0, which corresponds to approximately 107

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CFU/ml.

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2.2 Preparation of the AGFK germinator

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AGFK (L-asparagine, D-glucose, D-fructose, KCl) (Sigma Aldrich, Co., St.

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Louis, MO, USA) solution was prepared with 25 mM Tris-HCl buffer (pH 7.4). To

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evaluate the effects of different AGFK concentrations on the germination of C.

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perfringens spores, different concentrations of AGFK were designed as shown in

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Table 1. Insert Table.1 goes here

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2.3 Spore germination indicators of C. perfringens

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2.3.1 The spore germination rate of C. perfringens

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Spores lose heat resistance and release almost all Ca2+-DPA upon the completion

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of germination (Setlow, 2019). Spores were incubated at a concentration of 107

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spores/ml in reactions containing germination buffer (25 mM Tris-HCl [pH 7.4]) and

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AGFK for 80 min at 37℃, then subjected to 80℃ for 20 min to kill the existing

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vegetative cells. Each sample was serially (1:10) diluted with sterile peptone water

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and pour-plated on tryptose sulfite cycloserine agar base (TSC) medium. Plates were

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incubated at 37℃ for 24 h. After incubation, the number of surviving spores was

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determined by the viable cell count method every 10 min for 80 min. The spore

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germination rate (Srate) was calculated following Eq. 1

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S

rate

=

S

Total

− S survival

S

× 100%

(1)

Total

162

where Srate is the spore germination rate; Stotal is the total spore viable count

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before heat treatment; and Ssurvival is the number of spores remaining after heat

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treatment.

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2.3.2 OD600 changes during C. perfringens spore germination

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The extent of spore germination was calculated by measuring the decrease in 7

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OD600, and was expressed as a percentage of the initial OD600. The spore

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germinability can be expressed by the maximum rate of the reduction in OD600 to the

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initial value. To evaluate the effects of different AGFK concentrations on the rate of

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germination, spore suspensions were heat activated at 75°C for 20 min, cooled in

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ambient temperature water for 5 min, and then incubated in a 37°C water bath, as

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previously described. Heat-activated spores (OD600 of 1.0) were incubated with

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pre-warmed AGFK solution at 37°C for 80 min in a total volume of 0.2 ml in 96-well

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microtiter plates. Spore germination was routinely measured by monitoring the

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change in OD600 using a microplate reader (Molecular Devices, SpectraMax M3). The

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extent of spore germination was calculated by measuring the decrease in OD600, and

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was expressed as a percentage of the initial OD600 (Eq (2)). Thus, the spore

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germination rate was expressed as the maximum rate of the reduction in OD600 for the

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spore suspension relative to the initial value. A ~60% decrease in OD600 indicated

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complete spore germination, as determined in previous studies (Daniel et al., 2008 ).

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The levels of germination were also confirmed by phase-contrast microscopy every

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10 min following inoculation; germinated spores change from phase-bright to

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phase-dark. All values were averaged from two experiments performed on at least two

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independent spore preparations. Individual values varied by 15% from the average

185

values.

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OD600 % =

187 188 189

D D

d

×100%

(2)

i

Where OD600% is the percentage of the initial OD600, OD600, and

D

i

D

d

is the decrease in

is the initial OD600.

2.3 3 The release of Ca2+-DPA during C. perfringens spore germination

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To measure the Ca2+-DPA% during AGFK-induced spore germination, heat

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activated (75°C, 20 min) spores (OD600 of 1.5) were incubated with pre-warmed

192

AGFK of differing concentrations (Tris-HCl, pH=7.4) at 37°C. One ml aliquots of

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germinating solutions were centrifuged for 3 min at 13,200 rpm in a microcentrifuge

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tube, and the amount of Ca2+-DPA in the supernatants was determined by measuring 8

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absorbance at 270 nm (A270), as previously described (Saeed., et al,2013 ). The initial

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content of Ca2+-DPA in spore preparations was measured by boiling 1 ml aliquots of

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germinating spores for 60 min, centrifuging for 5 min in a micro-centrifuge at 13,200

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rpm, and measuring the A270 of the supernatants (Eq (3)). A previous study indicated

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that ~90% of the material absorbing at A270 contained Ca2+-DPA in C. perfringens

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spores (Setlow, 2008). All values reported are the average of five replicates performed

201

from at least three independent spore preparations.

202

203

Ca2+-DPA%=

F F

d

×100%

(3)

i

Where Ca2+-DPA% is the percentage of the initial Ca2+-DPA,

F

d

is the release

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of Ca2+-DPA% by spores of C. perfringens during germination with AGFK, and

205

is the initial Ca2+-DPA.

206

2.4 Acquisition and processing of microscopic hyperspectral images

F

i

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The microscopic HSIT device and its schematic diagram are shown in Fig. 1.

208

The main imaging system consists of a microscope (Nikon 80i, Nikon Corp.),

209

transmission grating spectrometer (PGP-prism-raster-prism structure, Sichuan Dualix

210

Spectral Imaging Technology Co., Ltd.), a high-density charge coupled device (Sony

211

ICX674 CCD, pixel resolution 1936*1456 pixels, pixel size of 4.54 µm*4.54 µm), a

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parallel moving stage, data acquisition and pre-processing software (SpectraCube,

213

Auto Vision Inc., USA), a control module, and a computer.

214

Fig. 1

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The flow chart of microscopic hyperspectral image analysis is shown in Fig. 2.

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The process consisted of six steps, including (1) the acquisition of microscopy

217

hyperspectral images; (2) extraction of the hyperspectral 3-D data cube and

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preprocessing; (3) extraction of the optimum intervals and feature variables from the

219

spectral information using synergy interval partial least square (siPLS) and genetic

220

algorithm (GA); (4) extraction of image data and analysis by the Gray level

221

co-occurrence matrix (GLCM); (5) building calibration models based on the selected

222

wavelengths, the optimum image feature variable, and data fusion of image and 9

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spectral features using GA-PLS; and (6) the selected optimal prediction model, and

224

model validation. Fig. 2

225 226

2.4.1 Image acquisition and correction

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Spore preparation and germination are described in Sections 2.1. Heat-activated

228

spores (OD600 of 1.0) were incubated with the optimum concentration of pre-warmed

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AGFK at 37°C for 80 min. A spore volume of 0.2 ml was placed on a glass slide

230

every 10 min for 80 min.

231

Samples were acquired by microscopic HSIT every 10 min for 80 min. The total

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192 samples were acquired by microscopic HSIT at four AGFK concentrations (0, 50,

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100 and 200 mM/L). The spectral range of the microscopic hyperspectral imager was

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350-850 nm. The spectral resolution of the imager was < 2 nm. The width and length

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of the slit were 30 µm and 14.2 mm, respectively. The spectral sample step was 0.3

236

nm. The electronic precision displacement table scanning stroke was 30 mm. The

237

microscopic hyperspectral data of the C. perfringens spores collected by the imager

238

was then visualized as a 3-D cube or a stack of multiple 2-D images. Each pixel of the

239

image had two attributes, including the intensity and spectrum. Consequently, the

240

spore change was analyzed from both the spatial and spectral angles. The collected

241

data were transmitted by RS442-USB to a computer. When the system performed

242

push-sweep hyperspectral imaging, the spatial dimension × spectral dimension was

243

merged into pixels (merged pixels: 960 × 176) to improve the acquisition efficiency.

244

In the process of microscopic HSIT cube acquisition, the acquired images could

245

be affected by the illumination system, transmission rate of incident light, and other

246

possible variations in the system (Labitzke and Bayraktar et al., 2013). The

247

reflectivity in the original data was corrected by the standard reflection white board

248

(Fig. 2 (b)). To allow error correction and to obtain a relative reflectance, a dark

249

image and a white image were obtained to normalize the hyperspectral image data, as

250

shown in Eq. 4.

251

R

λ

=

I W

−

λ λ

−

(4)

Bλ Bλ 10

252 253

Where

R λ is the relative reflectance value after normalization, I λ is the

original data, B is the dark image (approximately 0% reflectance) recorded by λ

254

covering the lens caps of the camera, and W λ is the white reference image obtained

255

by allowing all light to pass (approximately 99% reflectance).

256

2.4.2 Spectral data extraction and preprocessing

257

A quadrate region of interest (ROI) with a size of 10 × 10 pixels was selected in

258

the spatial range. The spectrum for each spore and the vegetative cell of C.

259

perfringens during the germination process was calculated by averaging the spectral

260

response of each pixel in the ROI (as shown in Fig. S1). Standard Normal Variate

261

(SNV) is a routinely adopted pretreatment method in spectroscopy and transforms

262

each spectrum to a zero mean-intensity value with unit standard deviation (Xiaobo

263

and Jiewen et al., 2007). According to our previously study (Chen and Jiang et al.,

264

2010), SNV is suitable for the spectra of microbial samples. Thus, in this study,

265

smoothing was performed by light scattering of each spectrum with a 9-point mean

266

filter and SNV to eliminate variations in the baseline. The mean spectral data for each

267

sample were used in the next stage of the analysis. Therefore, only the wavelength

268

region of 350-850 nm with a total of 176 spectral bands was considered effective and

269

used in this study. A total of 192 spectra under different AGFK concentrations ( 50

270

100 and 200 mM/mL) were collected every 10 minutes for 80 min during spore

271

germination.

272

To further extract the spectral information related to the Srate, OD600% and

273

Ca2+-DPA% of spores, principle component analysis (PCA) was used to extract the

274

spectral features from the microscopic HSI data of the selected wavelength regions.

275

GA and siPLS were employed to select the most informative wavelengths correlated

276

with C. perfringens spore germination (Fig. 2 (c)-(f)). The GA-PLS algorithm was

277

used to construct calibration models based on the selected feature information (Fig. 2

278

(g)). The root mean square error of the cross validation (RMSECV), root mean square

279

error of the prediction (RMSEP), and correlation coefficients of each model of the

280

calibration data set (Rc), as well as the prediction data set (Rp) were taken into 11

281

account.

282

2.4.3 Extraction of optimum features in spatial way (in Fig.2 (h))

283

2.4.3.1 Characteristic image extracted by PCA

284

Spore germination was revealed by the transformation of spores from

285

phase-bright to dark-phase using phase-contrast microscopy (Paredes-Sabja and

286

Setlow et al., 2011). Consequently, the change in spore images can provide a better

287

understanding of the dynamic germination process. The band-to-band correlation

288

possibly creates redundant information in the microscopic HSI data. PCA was used to

289

infer the uncorrelated principal components and to maximize the representation of the

290

original data (Sharifi and Langari, 2017). Thus, PCA was used to reduce the spectral

291

dimension of the microscopic hyperspectral reflectance images of the spores (Fig. 2

292

(i)). The top principal component (PC) images that expressed the information of

293

original data were identified based on their variance contributions. Each PC image is

294

the linear sum of the original images at individual wavelengths, multiplied by the

295

corresponding (spectral) weighting coefficients (Eq. 5) (Zhu and Yao et al., 2016).

296

Two or three bands with higher (local maximum) weighting coefficients were selected

297

from the PC images as the dominant bands.

298

PCm =

n

∑a I i =1

i

(5) i

299

Where PCm is the mth PC image, n is the number of pictures in the original MHSI

300

data, ai is the weight coefficient for the picture at the ith waveband, and Ii is the

301

original image at the ith waveband.

302

2.4.3.2 Extraction of optimal feature variables from PC images

303

The universal hyperspectral image analysis methods use abundant spectral

304

information for image segmentation, but lack information about the spatial structure

305

of images. For the hyperspectral images of C. perfringens spores, germination

306

characteristics cannot be easily differentiated by the human eye. Thus, spatial

307

information must be processed for the purpose of improving image segmentation

308

performance.

309

Under uniform arrangements, each spore sample could present its own image 12

310

attributes in the dominant waveband images. There are many different techniques

311

used to extract feature information from the images of spores during germination. The

312

gray level co-occurrence matrix (GLCM) has been widely used to extract image

313

feature information (Bi and Lin et al., 2019). Each element (i, j) in GLCM represents

314

the probability that two pixels with the gray level i and j co-occur in the image and are

315

separated by a distance. In this study, five image features, including contrast (Y1),

316

entropy (Y2), inertia (Y3), inverse difference moment (Y4) and correlation (Y5) were

317

extracted by GLCM analysis. Such features were calculated according to Eqs. (6)-(10).

318

The above mentioned parameters were calculated at different distances (from D = 1 to

319

10) and four directions (0, 45, 90, and 135°) for each pixel in the GLCM.

320 321 322 323

324

Y

1

=

Y

2

= −∑

Y

3

=

∑

i, j

Y

4

=

∑

i, j

Y

5

=

∑

∑

i, j

i− j

i, j

2

(6)

p (i, j )

(7)

p ( i , j ) ln p ( i , j )

[ p ( i , j ) × (i −

j)

2

(8)

]

(9)

p (i, j ) 1+ (i − i, j

(i − j )

µ

i

2

µ σ σ

)( j −

i

j

) p (i,

j)

2

(10)

j

325

Where the i and j are the elements of the gray level. The p (i, j) denotes the value

326

of the probability function (relative frequencies). The ìi and ìj are the mean values and

327

σ and σ are the associated variances. i

328

j

2.4.4 Data fusion and building prediction models by GA-PLS (Fig. 2 (l) and (m))

329

As mentioned above, the germination of bacterial spores is initiated when they

330

sense compounds termed germinants. Biophysical events affect the spore’s shape,

331

color and texture, including the release of monovalent cations (H+, Na+, and K+), Ca2+

332

- DPA release by the spore core, hydrolysis of the spore’s peptidoglycan cortex by

333

either of two redundant enzymes, completion of cortex hydrolysis, germ cell wall

334

expansion, spore core hydration, and the resumption of spore metabolism and

335

macromolecular synthesis (Setlow, 2019). 13

336

The samples treated with AGFK were analyzed by microscopic HSI, and data

337

fusion was used to predict the Srate, OD600%, and Ca2+-DPA % of C. perfringens

338

spores. Generally, the data fusion method can be categorized into three levels;

339

low-level fusion (LLF), intermediate-level fusion (ILF) and high-level fusion (HLF)

340

(Korel and Luzuriaga et al., 2001). ILF, also known as feature level fusion, is the

341

integration of the feature variables of each sensor, which can maintain original

342

information. ILF was employed in this study to fuse the features of images and

343

spectra for further analysis.

344

The spectral and image variables were extracted from the C. perfringens spore

345

during the spore germination process. The corresponding values of Srate, OD600%, and

346

Ca2+-DPA % were determined using multivariate data analysis methods. In order to

347

highlight the advantage of the microscopic HSIT for the detection of the Srate, OD600%,

348

and Ca2+-DPA % during the spore germination process, the data fusion strategy was

349

used to build the prediction model that used the spectral and image variables created

350

by GA-PLS.

351

GA-PLS is the most widely used method for spectroscopic data analysis. It

352

combines the advantages of GA and PLS (Kawamura and Tsujimoto et al., 2019). GA

353

is a popular heuristic optimization technique that employs a probabilistic, non-local

354

search process inspired by Darwin's theory of natural selection (Song and Li et al.,

355

2019). The calibration models were selected by the GA-PLS. Finally, the prediction

356

set parameters, RMSEP and Rp were used to evaluate the Srate, OD600%, and

357

Ca2+-DPA % calibration models, and thus, the optimal model for each indicator could

358

be obtained. The optimal model was tested by an independent prediction set. The

359

performance of optimal model for the prediction set was evaluated according to the

360

root mean square error of Cross-Validation (RMSECV) and the regression coefficient

361

(Rcv) in the prediction set.

362

2.5 Software

363

All image processing and data analysis procedures were executed using

364

programs developed in Matlab 7.0 (the MathWorks, Natick, MA, USA). The

365

extraction of reflectance spectral data from hyperspectral images was accomplished 14

366

using ENVI 4.3 software (ITT Visual Information Solutions, Boulder, CO, USA).

367

3. Results and Discussion

368

3.1 The spore germination rate of C. perfringens

369

The concentration of germinant solution plays a critical role in initiating spore

370

germination in C. perfringens. Thus, the rate of AGFK-induced spore germination

371

was dependent on the AGFK concentration. To determine the maximum germination

372

at specific concentrations of AGFK, the Srate was examined at 0, 50, 100, and 200

373

mM/mL of AGFK. Different AGFK concentrations had significant effects on the Srate

374

of C. perfringens spores. The Srate of the AGFK-induced spores increased with

375

increasing treatment time, and spores exhibited significant differences (p < 0.05) in

376

their maximum germination rates at AGFK concentrations ranging from 0~200

377

mM/mL (Table. 2). The maximum germination response was observed at an AGFK

378

concentration of 100 mM/mL. After 80 min, the Srate was 94.59% with 100 mM/mL of

379

AGFK. However, there was no change in the Srate of spores after 100 min of AGFK

380

treatment (p>0.05), indicating that AGFK-induced spore germination ended after 80

381

min.

382 383

Insert Table 2 here 3. 2 The change in OD600% during C. perfringens spore germination

384

Fig. 3 shows the effect of time and concentration AGFK on germination of C.

385

perfringens spores. The germination of C. perfringens spores under different

386

concentration AGFK was shown in Fig.3 (a). The germinability has significant

387

difference. With C. perfringens spores incubated in 100 mM/mL AGFK, a 50–60%

388

decrease in OD600 was observed after 60 min, and >95% of the spores became dark

389

under phase-contrast microscopy, indicating complete germination (Fig.3 (c)). Based

390

on the change in OD600, the maximum rate of C. perfringens spore germination was

391

observed at AGFK concentrations of 100 mM/mL (Fig. 3 (b)). Strikingly, the

392

germination level with 100 mM/mL AGFK was higher than at other concentrations. In

393

contrast, no decrease in OD600 was observed with C1 spores incubated with 0 mM/L

394

AGFK for 60 min. Those differences were confirmed by examining spore cultures

395

through phase-contrast microscopy (Fig. 3(c)), which showed that after incubation for 15

396

80 min with 100 mM/mL AGFK, > 90% of C. perfringens spores had germinated. Fig. 3

397 398

3.3 The release of Ca2+-DPA during C. perfringens spore germination

399

Nutrient germinants can combine with the receptors located in the spore’s inner

400

membrane. Following such an event, spores can release a variety of compounds from

401

the core, including most notably, Ca2+-DPA, which comprises 20% of the spore core’s

402

dry weight (Setlow, 2003). Most of the DPA is released as Ca2+-DPA and can activate

403

downstream germination events. To gain more insight into the effects of different

404

concentrations of AGFK on germination, the release of Ca2+-DPA was measured (Fig.

405

4). During germination with 100 mM/mL of AGFK, C. perfringens spores released

406

nearly 72% of their DPA during the first 10 min, and 79% of their Ca2+-DPA after 80

407

min of incubation. In contrast, C. perfringens spores released significantly less

408

Ca2+-DPA during germination without AGFK. By comparison, the released Ca2+-DPA

409

was fastest with 100 mM/mL AGFK. Fig. 4

410 411

3.4

Microscopic

hypercube

dimensional

reduction

412

characteristic variables

413

3.4.1 Microscopic hypercube dimensional reduction

and

extraction

of

414

Microscopic HSIT was used to determine the Srate, OD600%, and Ca2+-DPA %

415

prediction model under germination with 100 mM/mL AGFK. All samples were

416

divided into two sets along the time sequence under different concentrations. One of

417

three samples was assigned to the prediction set, and the remaining two samples were

418

assigned to the calibration set. Thus, the calibration set contained 128 samples and the

419

prediction set contained 64 samples. As shown in Table 3, the ranges of the reference

420

measurements, namely the Srate, OD600%, and Ca2+-DPA % in the calibration set,

421

almost covered the range of the prediction set. Comparisons between the standard

422

deviations obtained for each parameter in the calibration and prediction sets were not

423

significantly different (p > 0.05). Therefore, the distribution of samples was

424

appropriate in the calibration and prediction sets.

425

Insert Table 3 here 16

426

The original microscopic HSI data cannot be directly used due to the volume of

427

redundant information. Thus, PCA was first used to reduce the dimensions of the

428

microscopic HSI data. The results showed that only the top three PCs (i.e., PC1, PC2

429

and PC3 with a total accumulative contribution rate 99.79%) issued from the PCA

430

were considered in the subsequent analysis.

431

3.4.2 Selection of optimum spectral variables

432

During spore germination, the internal characteristics (e.g., chemical

433

composition, Ca2+-DPA release, tissue structure, etc.) of spores are constantly

434

changing, which can be reflected in the spectral signals. Hence, the spectra extracted

435

from the microscopic hyperspectral data cube can explain the Srate, OD600%, and

436

Ca2+-DPA % during spore germination. The mean reflectance spectrum for each

437

image was calculated by averaging the spectral responses of each pixel in the

438

pre-selected ROI. Fig. 5(a) presents the raw spectra of spores during germination,

439

which need to be pre-processed because of the interference of slope variation and

440

light scatter. Here, SNV was used to remove such slope variation and scatter effects

441

(Chen, Jiang & Zhao, 2010), and the pre-processed spectra are shown in Fig. 5(b).

442

The full spectra that were used to develop the regression model increased the model’s

443

complexity, while one single interval from the spectrum may cause insufficient

444

accuracy and stability of model. Therefore, the siPLS algorithm was employed to

445

select the optimum combination of several intervals that were highly related to the

446

fitting attributes.

447

First, the spectral regions were divided into equidistant subintervals, from 10 to

448

20 intervals; second, the number of subintervals was set to 2, 3, or 4. For the Srate, the

449

lowest RMSECV was achieved when the full spectra were split into 15 intervals and

450

the optimum combination of intervals was [6 8 10 11], corresponding to 484.2-503.0

451

nm, 526.6-545.6 nm, 569.5-588.8 nm, and 591.1-610.4 nm, respectively, as shown in

452

Fig. 5 (c). For the OD600%, the combined intervals selected by siPLS are presented in

453

Fig. 5 (d), where the full spectra were split into 16 intervals and the optimum

454

combination was [4 6 12 13], with corresponding frequency regions of 442.3-460.9

455

nm, 484.2-503.0 nm, 612.8-632.2, and 634.6-654.1 nm, respectively. For Ca2+-DPA %, 17

456

the optimum combination was [5 7 8 10], corresponding to 463.2-481.9 nm,

457

505.4-524.3 nm, 526.6-545.6 nm, and 569.5-588.7 nm, respectively, as shown in Fig.

458

5 (e). The selected parameters are shown in Table. 4.

459

Fig. 5

460

Insert Tab. 4 here

461

Although the variables selected by siPLS were much less than that contained in

462

the full spectrum, further reductions were still required and strong co-linearity was

463

observed between variables in the same intervals. GA is an adaptive heuristic search

464

algorithm that can be applied for spectral variable selections, and is combined with

465

siPLS to form the GA-siPLS algorithm (Leardi and González, 1998). Therefore, GA

466

was employed to select the variables selected by the previous siPLS. GA was carried

467

out for 100 generations with 200 chromosomes each. Moreover, the algorithm was

468

repeated 10 times, starting from different random initial populations. The best

469

solution, resulting from 10 of GA trials performed, was retained. In the spectra, the

470

global maximums of the correlation coefficient (CV) for the Srate, OD600%, and

471

Ca2+-DPA % of the calibration model modified with the spectral variables selected by

472

GA were 84.27%, 87.44%, and 89.65%, respectively. The final selected intervals and

473

variables were also subjected to minimum RMSECV in the calibration PLS model.

474

The optimum GA-PLS model, with 12, four and seven PLS factors, was evaluated by

475

the prediction set, and generated satisfactory predictive performance, with a RMSEP

476

of 0.93, 24.36, 58.98, and a Rp of 0.94, 0.89, 0.92, for the Srate, OD600%, and

477

Ca2+-DPA, respectively (as shown in Table 5).

478

3.4.3 Extraction of the optimum spatial variables of spores during germination

479

There is no doubt that spore structure and morphology changed during

480

germination (Rao and Feeherry et al., 2018). With spore germination induced by

481

AGFK treatment, spore structure and morphology gradually changed because AGFK

482

germinants bound to receptors in the spore’s inner membrane. Such interactions

483

trigger the release of the spore core’s large depot of dipicolinic acid and cations, as

484

well as the hydrolysis of the spore’s peptidoglycan cortex by either of two redundant

485

enzymes in C. perfringens. The spore germinates and progresses through outgrowth, 18

486

ultimately being converted back into a growing cell. At the same time, its color

487

becomes dark, its texture becomes obscure, and its morphology gradually turns from

488

spherical to short rod-shaped during spore germination process (Setlow, 2008). Hence,

489

the microscopic HSIT can explain the changes over time during spore germination.

490

In this study, PCA was used to reduce the dimensions of the microscopic HSI

491

data. Therefore, only the top three PCs (i.e., PC1, PC2, and PC3 images) issued from

492

PCA were considered for further analysis. It was also found that the PC1 image

493

provided the best representation of the original spore, with a variance contribution

494

rate of 96.34%. Thus, the dominant bands were determined according to the

495

weighting coefficients of the PC1 image. Texture feature parameters from the top

496

three PCs images (Fig. 6) of spore germination over time in four directions (0, 45, 90,

497

and 135°) and at different distances (from D = 1 to 10) were calculated based on

498

GLCM. Five image features were extracted in each direction. Thus, 600 feature

499

variables based on GLCM texture analyses from the three dominant waveband images

500

were obtained. The optimum image variables were selected by the GA-PLS

501

algorithm.

502 503

Fig.6 3.5 Indicators predicted by GA-PLS using image features and spectra

504

As mentioned above, spore germination can cause changes to internal (e.g.,

505

chemical composition, DPA release, tissue structure, etc.) and external attributes (e.g.,

506

color, texture and form, etc.) of spores. The spectra extracted from the microscopic

507

HSI data cube can provide details of the changes of such internal and external

508

attributes of spores. The germination of C. perfringens spores is a dynamic process.

509

Hence, the relationships between the Srate, OD600%, and Ca2+-DPA% obtained during

510

the spore germination process, and spectral and spatial information from the

511

microscopic HSI data cube, were complicated and tended to show nonlinear data.

512

After the acquisition of the microscopic HSI, spectral data and image variables

513

of the calibration set were extracted by the GA-siPLS algorithm. The physicochemical

514

indexes of spores were determined every 10 min for 80 min by chemical analytical

515

methods after the addition of different concentrations of the AGFK germinator. The 19

516

GA-PLS algorithm was used to build calibration models for the Srate, OD600%, and

517

Ca2+-DPA % of spores based on the optimal selected variables from the spectral data,

518

the screening images, and their combinations. The results are shown in Table 5.

519

As observed the Table 5, based on the optimal wavelengths, the Srate calibration

520

was built and yielded acceptable results (Rc = 0.96, RMSEC = 0.64, Rcv = 0.93,

521

RMSEP = 0.87, Rp = 0.94). The optimum prediction model was built by GA-PLS

522

using data fusion variables. For the OD600%, the optimal model was built by GA-PLS

523

using image variables. The OD600% calibration yielded acceptable results (Rc = 0.93,

524

RMSEC = 19.36, Rcv = 0.91, RMSEP = 24.36, Rp = 0.89). For Ca2+-DPA %, the model

525

based on the fusion of spectral and imaging data was optimal. The Ca2+-DPA %

526

calibration yielded acceptable results (Rc = 0.95, RMSEC = 49.83, Rcv = 0.93, RMSEP

527

= 58.98, Rp = 0.92). These results could be explained by the following: spore

528

germination is a complex and dynamic process as spore germination is often

529

accompanied by changes to internal (e.g., chemical components, tissue structure, etc.)

530

and external attributes (e.g., texture, color, etc.) over time. For example, spore

531

brightness and refraction are gradually changing over time during the spore

532

germination process. Additionally, with the release of Ca2+-DPA, spore refraction and

533

OD600 change during the spore germination process. Thus, the changes to the Srate,

534

OD600%, and Ca2+-DPA % indicate that the spore germination process can be captured

535

by microscopic HSIT. At present, there are few reports on the prediction of spore

536

germination rates based on microscopic HSI and chemometric methods. The fusion of

537

spectral and image descriptors can collect more information than single data points,

538

and can fully evaluate the germination process of C. perfringens spores. Insert Table. 5 here

539 540

4. Conclusions

541

C. perfringens spores are important factors causing food spoilage and the

542

swelling of food bags. Following spore germination, the exotoxin is one of the

543

foodborne pathogens with strong pathogenicity. According to Srate, OD600%, and

544

Ca2+-DPA %, spore germination was analyzed under different AGFK concentrations.

545

Furthermore, this paper presents a novel method based on microscopic HSIT to 20

546

successfully predict the Srate, OD600%, and Ca2+-DPA % of spores at different AGFK

547

concentrations. The method simultaneously explores information from both spectral

548

and spatial data sources, enabling the analysis of chemical profiles and the external

549

form of spores during germination. It was also found that the analysis of multiple

550

forms of data using the GA-PLS algorithm can be used in other relative detection.

551

Additionally, the combination of microscopic HSIT with chemometrics provides a

552

nondestructive, real-time, and in situ measurement tool to significantly improve the

553

efficiency of preventative spore control and food safety assurance.

554

Disclosures

555

The authors have no relevant financial interests or conflicts of interest to

556

disclose.

557

Acknowledgements

558

This work was financially supported by the National Natural Science Foundation

559

of China (No. 31571856), the Henan Science and Technology Major Project (No.

560

161100110800), the key science and technology Support Program of Henan province

561

(No. 192102110216), National key r&d projects in the 13th five-year plan of China

562

(No.2018YFD0401200), and Nature science foundation of henan province

563

(152300410068).

564

( http://www.internationalscienceediting.com ) for editing this manuscript.

We

thank

International

Science

Editing

565 566 567

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24

Table. 1 Different concentrations of AGFK settings Lasparagine /mM

D-glucose /mM

D-fructose/mM

KCl /mM

100

10

10

50

100

10

10

100

100

10

10

200

Table 2 Results of different concentrations of AGFK on the spore germination rate of C. perfringens AGFK

C.perfringens spore (lg (CFU/ml))

concentration (mM/mL)

0 min

20 min

40 min

60 min

80 min

0

5.44±0.20bA

5.45±0.01aA

5.47±0.01aA

5.43±0.09aA

5.42±0.05aA

50

5.39±0.18aA

4.97±0.11aA

5.20±0.02bB

5.03±0.07dB

4.93±0.02bB

100

5.35±0.08cA

4.27±0.07bA

4.04±0.04cA

3.67±0.09aB

3.22±0.01cC

200

5.34±0.03cA

4.39±0.02bA

4.16±0.04cA

3.93±0.06cB

3.44±0.03cC

100 min 5.42±0.01aA 4.87±0.07bC 3.20±0.04cC

3.41±0.06cC Note: The a, b and c reflect a significant difference in the horizontal group (0-100 min );The A, B and C reflect a significant difference between groups (0-200 mM/mL).

Table 3. Reference measurement of the Srate, OD600%, and Ca2+-DPA % in the calibration and prediction set Spore

AGFK Range

germination

Subsets

N

Concentration

Mean

S.D.

(min-max) parameters

(mM/mL)

Srate

Calibration

128

Prediction

64

OD600%

Ca2+-DPA %

N: Sample number.

S.D.: standard deviation.

50

4.23-5.49

4.65

0.519

100

3.22-5.35

4.11

0.798

200

3.77-5.34

4.28

0.813

50

70.78-100

38.49 11.83

100

54.38-100

75.68 15.79

200

58.97-100

64.15 13.51

50

0-58.13

30.25 21.77

100

0-78.24

65.36 25.68

200

0-72.35

59.98 29.41

Table. 4 The optimum intervals of the Srate, OD600%, and Ca2+-DPA % were selected by siPLS The optimum indicators

Corresponding PLS factors

interval

Wavelength (nm) 484.2-503, 526.6-545.6,

Srate,

[6 8 10 11]

12

569.5-588.79 and 591.1610.4 442.3-460.9, 484.2-503,

OD600%

[4 6 12 13]

7 612.8-632.2 and 634.6-654.1

Ca2+-DPA %

463.2-481.9, 505.4-524.3,

[5 7 8 10]

7 526.6-545.6 and 569.5-588.7

Table 5 Performance of the Srate, OD600%, and Ca2+-DPA % of spore germination based on GA-PLS

Indicators Calibration

Cross-validation

prediction

PLS factors Model

Srate

GA-PLS

OD600%

Ca-DPA%

Model based on image Model based on spctral Model based on data fusion Model based on image Model based on spctral Model based on data fusion Model based on image Model based on spctral Model based on data fusion

Rc

RMSEC

Rcv

RMSECV

Rp

RMSEP

14 4

0.89 0.94

0.76 0.69

0.86 0.92

0.89 0.86

0.83 0.89

1.23 0.98

10

0.96

0.64

0.93

0.76

0.94

0.87

6 13 7 8 15

0.93 0.88 0.91 0.87 0.91

19.36 21.54 20.96 54.58 52.37

0.91 0.85 0.88 0.84 0.88

22.54 24.87 23.43 59.71 58.68

0.89

24.36

0.82 0.85 0.81 0.84

27.32 26.19 65.52 60.34

9

0.95

49.83

0.93

55.24

0.92

58.98

Note: Rc, coefficient of determination in calibration set. Rcv, coefficient of determination in validation set; Rp, coefficient of determination in prediction set.

Fig.1 The Microscopy Hyperspectral imaging system and schematic diagram of principle

Data correction

Image acquisition

(d)

(h)

Image information (external attributes)

Spectral information (internal attributes) (e)

(i)

siPLS

Characteristic picture

Optimal Spectral intervals (f)

extraction by PCA (j)

GA

Optimum texture feature

Feature variables (g)

Background removal

variable selected by GLCM GA-PLS

(k)

Build calibration models

GA-PLS

Build calibration models Data fusion

Different concentration AGFK

(l) Different germination time

The S, OD600% and Ca2+-DPA % calibration model by GA-PLS (m)

The optimum models of the S, OD600% and Ca2+-DPA %

model validation

Fig. 2 A flow chart of rapid prediction the C. perfringens spore germinability under different AGFK concentration.

(a)

0 min

(b)

20 min

40 min

60 min

80 min

(c) Fig.3 Effect of time and concentration AGFK on germination of C. perfringens spores. (a) Germination of C. perfringens spores under different concentration AGFK; (b) AGFK concentration dependence of C. perfringens spore germination. spores were germinated with various AGFK concentrations. The maximum rate of germination was obtained at 100 mM/mL. (c) The phase-contrast images of C. perfringens spores at 100 mM AGFK concentration. The bright of the spores became gradually dark over time under phase-contrast microscopy.

Fig. 4 The Ca2+-DPA % during germination of C. perfringens spores. Spores were heat activated and germinated at 37 °C in 25 mM sodium Tris-HCl (pH 7.4) with no germinant ( (

) or with 50 mM AGFK (

) 100 mM AGFK (

), or 200 mM AGFK

), and the Ca2+-DPA % was measured as described in Materials and Methods.

6

(a)

1.2

5

1.1

4

Reflectance

Reflectance

1.3

1 0.9

SNV

0.8

(b)

3 2 1

0.7

0

0.6

−1

0.5 350 400 450 500 550 600 650 700 750 800

−2 350 400 450 500 550 600 650 700 750 800

Wavelength (nm)

Wavelength (nm)

5

(c)

4 3 2 1 0 −1

400

450

500

550 600 650 700 Wavelength (nm)

750

800

Selected intervals [4 6 12 13] 5

(d)

4 3 2 1 0 −1

400

450

500

550 600 650 Wavelength (nm)

700

750

800

Response, raw data [ mscmean is used in the calculations]

Selected intervals [6 8 10 11]

Response, raw data [ mscmean is used in the calculations]

Response, raw data [ mscmean is used in the calculations]

siPLS Selected intervals [5 7 8 10] 5

(e)

4 3 2 1 0 −1

400

450

500

550 600 650 Wavelength (nm)

700

750

800

Fig.5 Selection of optimum spectral characteristic variables. (a) Raw spectra of spore sample; (b) SNV preprocessing spectra, (c) Optimal spectral intervals selected by GA-siPLS for the S was [6 8 10 11]; (d) Optimal spectral intervals selected by GA-siPLS for OD600% was [4 6 12 13 ]; and (e) Optimal spectral intervals selected by GA-siPLS for Ca2+-DPA % was [5 7 8 10].

Fig. 6 Three PCs images selected from a hypercube

Highlights
The effects of different AGFK concentrations (0, 50, 100, 200 mM/mL) on C. perfringens spore germination;
A novel rapid method for the measurement of spore germination rate based on microscopic hyperspectral imaging technology (HSIT) was proposed;
Multivariate analyses (GA-siPLS and GA-PLS) and the gray symbiotic matrix (GLCM) were used to extract highly correlated spectral and spatial descriptors from the time-series data from microscopic HSIT, respectively;
Single spectral, spatial signals and the data fusion of spectral and spatial information were used to predict the Srate, the OD600% and Ca2+-DPA % by GAPLS during spore germination process.

Author Contributions Yao-Di Zhu designed the study, finished the experiment, and drafted the manuscript; Jia-Ye-Zhang assisted the experiment and collected test data; Miao-Yun Li improve the research plan and revise the manuscript; Li-Jun Zhao revised the manuscript; Hong-rong Ren and Long-Gang Yan helped the experiment and collected test data; Gai-Ming Zhao helped improve the research plan; and Chao-Zhi Zhu guided the experiment.