Qualitative and quantitative assessment of cork anomalies using near infrared spectroscopy (NIRS)

Food Packaging and Shelf Life 24 (2020) 100490

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Qualitative and quantitative assessment of cork anomalies using near infrared spectroscopy (NIRS)

T

David Pérez-Terrazasa,b,*, José Ramón González-Adradosa, Mariola Sánchez-Gonzálezb a b

MONTES (School of Forest Engineering and Natural Resources), Universidad Politécnica de Madrid, C/ José Antonio Novais, 10, 28040, Madrid, Spain Spanish National Institute for Agricultural and Food Research and Technology (INIA), Ctra. de La Coruña, km 7.5, 28040, Madrid, Spain

ARTICLE INFO

ABSTRACT

Keywords: Cork Cork granulate Visual quality Yellow stain Corkback Lignified cork

The aim of this study was to develop applications of near infrared spectroscopy (NIRS) to detect certain anomalies in cork used in the manufacture of stoppers for wine bottling. 326 spectra of cork planks were used in a qualitative analysis to evaluate the feasibility of NIRS to discriminate between pure cork, yellow stain and lignified cork. Classification and validation errors varied between 0 % and 11.4 %. In addition, 360 spectra of cork granulate were used in a quantitative analysis to determine the percentage of yellow stain and corkback. A root mean standard error of cross validation (RMSECV) of 1.50 % was obtained for yellow stain percentage (R² = 99.62 %). For corkback percentage RMSECV was 2.96 % (R2 = 98.53 %). These results show that NIRS technology has potential, alongside traditional methods, to determine the quality of cork used to manufacture stoppers.

1. Introduction Cork is a biological material obtained from the cork oak tree (Quercus suber L.). It is used to produce several types of bio-based materials with applications in wine closures, construction, aerospace and others. It is a natural, renewable, sustainable raw material with low environmental impact (Pereira, 2007) and which is obtained without the need to fell the trees. The economic activities associated with cork form part of the bioeconomy and contribute to conserving an important Mediterranean ecosystem; cork oak woodlands where it has considerable economic importance (Sánchez-González & Pérez-Terrazas, 2018; Verkerk, Martinez de Arano, & Palahí, 2018). The main destination sector for cork is the wine industry. Approximately 65–70 % of wine bottles are sealed with stoppers made from cork (Carvalho, 2009; Lopes et al., 2012), which can be classified into two types: natural cork stoppers and technical cork stoppers. Natural cork stoppers are made from a single piece of natural cork while technical cork stoppers are made of cork granulate and comprise one or several pieces. In both cases, the cork planks and the cork granulate used to manufacture the stoppers must be free from anomalies such as yellow stain, corkback or lignified cork, among others. The yellow stain is an alteration of cork tissue caused by Armillaria mellea (Vahl. Ex Fr.), a saprophytic basidiomycetes, that grows on soil and lignocellulosic materials (Pereira, 2007). In the regions of the tissue where the cork is attacked by this

fungus, cork shows a yellowish discoloration due to the degradation of tannins (García-Vallejo, Varea, Cadahía, & Fernández-de-Simón, 2001; Rocha, Delgadillo, & Ferrer-Correia, 1996) along with a biosynthesis of 2,4,6-Trichloroanisole or TCA (Moio et al., 1998). TCA is perceived at extremely low levels, 1.4−4 ng L−1 (Garcia, Lopes, De Barros, & Ilharco, 2015) and it has a mould-like taste that will be present in the wine (Juanola et al., 2004; Juanola, Subirà, Salvado, Garcia-Regueiro, & Anticó, 2005; Rocha, Delgadillo, Ferrer Correia, Barros, & Wells, 1998). Therefore, yellow stained cork should never enter the wine stoppers manufacturing supply chain. Lignified cork is cork with sclerenchyma formations not related to the lenticelar channels (Prades, Cardillo-Amo, Beira-Dávila, Serrano-Crespín, & Nuñez-Sánchez, 2017) and corkback is the phloemic tissue remaining on the outer side of the cork with very similar chemical composition and characteristics to the lignified cork (Pereira, 2007). Both of them modify the density of the cork and therefore affect the mechanical behavior of the stopper (Anjos, Rodrigues, Morais, & Pereira, 2014). Therefore, the less percentage of lignified cork or corkback a stopper has the better mechanical performance will have during the sealing period. At present, quality control for these anomalies is only standardized for cork stoppers (ISO-16419, 2013; ISO-20752, 2014ISO-20752, 2014; ISO-22308, 2005). However, these techniques cannot be applied to the raw material (planks or granulate) due to the high cost and high variability of the material (Pérez-Terrazas, González-Adrados, & Sánchez-González, 2018). Hence,

⁎ Corresponding author at: MONTES (School of Forest Engineering and Natural Resources), Universidad Politécnica de Madrid, C/ José Antonio Novais, 10, 28040, Madrid, Spain. E-mail address: [email protected] (D. Pérez-Terrazas).

https://doi.org/10.1016/j.fpsl.2020.100490 Received 6 June 2019; Received in revised form 11 February 2020; Accepted 14 February 2020 2214-2894/ © 2020 Elsevier Ltd. All rights reserved.

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quality control is performed using traditional visual methods in cork planks before being ground; in the case of granulate, non-standardized methods are applied in relatively small size. Near infrared spectroscopy (NIRS) is a non-destructive technique which is commonly used for quality control in the food and agricultural industry (Burns & Ciurczak, 2007; Shenk, Workman, & Westerhaus, 2008). Moreover, NIRS has the advantages of being quick to perform, requiring only small sample sizes, low analysis cost as well as providing information on different variables simultaneously. In the forestry sector, the NIRS technique has been used since 2006 to detect multi-traits of chemical, physical, mechanical and anatomical properties of wood and paper (He & Hu, 2013; Poke, Wright, & Raymond, 2005; Tsuchikawa & Kobori, 2015; Tsuchikawa & Schwanninger, 2013; Tsuchikawa, 2007). As regards cork, the primary use of NIRS is the characterization of cork planks according to visual quality, porosity and moisture content (Prades, García-Olmo, Romero-Prieto, Ceca, & López-Luque, 2010). Other applications include predicting the geographical origin of cork planks and stoppers (Prades, Gomez-Sanchez, Garcia-Olmo, & Gonzalez-Adrados, 2012), determining the moisture content along with various chemical, physical and mechanical parameters in cork stoppers (Prades, GómezSánchez, García-Olmo, González-Hernández, & González-Adrados, 2014) and the prediction of cork plank porosity before and after boiling (Sánchez-González, Garcıa-Olmo, & Prades, 2016). Two recent studies have focused on evaluating the ability of NIRS technology to identify different cork anomalies. The first of these studies developed NIRS models to evaluate anomalies such as earthy cork, stained cork, lignified cork or green cork in cork planks (Prades et al., 2017). The second evaluates the feasibility of using NIRS to detect and predict the percentage of yellow stain on cork granulate (Pérez-Terrazas et al., 2018). In both cases, the results suggest that NIRS technology can help to evaluate anomalies in cork planks and cork granulate. Therefore, the aim of this study is to develop NIRS applications to detect certain anomalies in different types of raw cork samples. A qualitative analysis is performed to detect the presence of yellow stain and lignified cork in cork planks, complementing the use of traditional methods to evaluate anomalies. In addition, quantitative analysis is developed to predict the percentages of yellow stain and corkback in samples of cork granulate. The critical level and detection limit for these cork granulate anomalies are also studied.

Fig. 1. Transverse section of cork planks used for qualitative NIRS analysis with different acquisition spots marked to collect the spectra. From top to bottom: (A) High quality cork plank (pure cork, PC), (B) Cork plank with yellow stain (YS) and (C) Cork plank with lignified cork (LC). Table 1 Number of: cork planks, acquisition spots (a spectrum at each acquisition spots), classification and validation spectra used for qualitative NIRS analysis. Type

Pure cork (PC) Yellow stain (YS) Lignified cork (LC) Total

Cork planks

19 43 28 90

Acquisition spots

118 108 100 326

Number of spectra Classification

Validation

83 75 71 229

35 33 29 97

2.1.2. Granulate samples and sample preparation Spectra for quantitative NIRS analysis were obtained from cork granulate taken from 100 cork planks of the INIA-CIFOR cork laboratory collection with approximately the same dimensions described above. All cork planks used in this study were gathered in a sampling carried out in Catalonia (Spain) in 1991. In this case only two types of cork planks were selected: 60 high quality planks (PC), completely free of anomalies, and 40 stained planks where yellow stain was clearly present (YS) (Table 2). 8 strips of 0.3 cm thickness (20 × 0.3 × 3.5 cm) were cut from the transverse section of planks. Pure cork offcuts and corkback offcuts were obtained from the PC slices, while yellow stained offcuts were separated from the YS slices. Consequently sufficient amounts of pure cork, corkback and stained cork offcuts were separately ground and sieved (< 0.5, 0.5–1 and > 1 mm) in order to obtain cork granulate of three types: cork without anomalies (G-PC) and corkback (G-CB) from the slices of PC planks, and yellow stained cork (G-YS) from the slices of YS planks. So that the samples were as homogeneous as possible and did not influence variables other than those studied, only the 0.5−1 mm granulate was used. Corkback was used instead of lignified cork because it was not possible to obtain sufficient amount of lignified

2. Material and methods 2.1. Samples and sample preparation 2.1.1. Plank samples Spectra for qualitative NIRS analysis were obtained from a total of 326 acquisition spots (0.5 cm in diameter), located at different positions of 90 cork planks with approximate dimensions of 20 × 20 × 3.5 cm. A spectrum was taken at each of the acquisition spots. The cork planks were gathered in sampling carried out in Andalucía (Spain) in 1991 and form part of the INIA-CIFOR cork laboratory collection. The quality of the cork planks for the manufacture of cork stoppers depends on the caliber and visual appearance (presence of anomalies and porosity). In this part of the study, only the visual appearance has been taken into account for evaluate the quality of planks. Thus, three groups of planks were selected taking into consideration ease of identifying points with or without the anomalies to be studied: 19 high quality planks with the lowest porosity and no anomalies (pure cork, PC), 43 planks with yellow stain clearly present (yellow stain, YS) and 28 planks with lignified cork clearly present (lignified cork, LC). On each plank a variable number of acquisition spots (between 1–9) was marked at points where most of the spot contained only one of the types of cork described above (Fig. 1). Table 1 shows the number of cork planks and number of acquisition spots selected per group.

Table 2 Number of cork planks and strips used for the quantitative NIRS analysis for each of the groups.

2

Type

Cork planks

Strips

Pure cork (PC) Yellow stain (YS) Total

60 40 100

480 320 800

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(Bruker, 2011b). The spectra from spots on the transverse section of cork planks were divided randomly and proportionally into classification and validation group, with approximately 70 % and 30 % respectively for pure cork, yellow stain and lignified cork (Table 1). The classification group was used to create a reference library composed of one average classification spectrum for each of the 3 groups. The algorithm used to develop the identification method was factorization (Eq. (1)).

Table 3 Number of samples by percentage of anomaly (a spectrum at each sample) for the quantitative NIRS analysis for each of the groups. Percentage of anomaly (%)

0 1 2 3 4 5 6 7 8 9 10 15 25 35 50 100 Total

Samples Yellow stain

Corkback

10 10 10 10 10 10 10 10 10 10 10 15 15 15 15 10 180

10 10 10 10 10 10 10 10 10 10 10 15 15 15 15 10 180

a = T1a * f1 + T2a * f2 + T3a * f3

(1)

This algorithm is based on principal component analysis (PCA) and represents a spectrum (a) as linear combinations of loadings or factor spectra (f) which are orthogonal to each other (Bruker, 2011b). T indicates the coefficients or scores required to reconstruct the original spectrum and subscripts 1, 2 and 3 indicate the number of the principal component. The effect of a loading on the reproduction of classification spectra is indicated by its eigen value. High eigen value indicates an important effect on the reproduction of classification spectra. For classification spectra identification, the spectral distance of each classification spectrum was compared with the threshold of its group. The threshold (Th) was calculated for each group with the following equation (Eq. (2)).

cork granulate and because it is the most frequent anomaly present in cork granulate. With this type of granulates, two different groups of samples were prepared. Firstly, yellow stain samples were prepared mixing yellow stained (G-YS) and pure cork (G-PC) granulates at different proportions, as described in our previous work (Pérez-Terrazas et al., 2018). The number of categories has been now increased to improve the accuracy and the discrimination capacity of the method. A total of 16 categories were defined, with the following yellow stain percentages: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 25, 35, 50 and 100 %. These percentages were established such that the range was as large as possible while at the same time having a greater incidence of lower values, in order to allow predictions in the whole theoretically possible range. A set of 180 samples was prepared taking 10 samples for categories from 0 to 10 % and 100 % and 15 samples for the rest (15, 25, 35 and 50 %) (Table 3). Initially the number of samples was going to be 10 for all cases, but since there was enough granulate, it was extended in some cases to 15 in order to improve accuracy. The volume of granulate per sample was fixed at 30 milliliters. Secondly, corkback samples were prepared mixing corkback (G-CB) and pure cork (G-PC) granulates at different proportions. The percentages of corkback and volume of samples were the same as describes above for yellow stain samples. The number of samples was also 180 (Table 3).

Th = Dmax + S0 * x

(2)

Where Dmax is the maximum spectral distance and S0 is the standard deviation respectively for each group. In this study we set an x of 0.25. If the spectral distance was less than the threshold, the spectrum was uniquely identified. In contrast, if the spectral distance was larger than the threshold, the spectrum was not identified. To determine the accuracy of the analysis, the classification error was determined. A partial classification error was calculated for each group as the ratio between the number classification spectra not identified and the total number of classification spectra in this group. Total classification error was also calculated as the ratio between the total number of classification spectra not identified and the total number of classification spectra. In addition, to discriminate between groups, we used the selectivity parameter (S), defined as the ratio of the distance between average classification spectra of two groups (D1-2) and the sum of threshold values from each of the groups (Th1 and Th2) (Eq. (3)):

S=

D1 2 (Th1 + Th2 )

(3)

S < 1: Overlapping between groups; S = 1: Groups in contact; S > 1: Groups separated. Finally, we used the validation error to validate the identification method. A spectrum was correctly classified if the spectral distance was smaller than the threshold for its group. The partial validation error was calculated for each group as the ratio between the number of validation spectra not identified and the total number of validation spectra in this group. The total validation error was also calculated as the ratio between the total number of validation spectra not identified and the total number of validation spectra.

2.2. Near infrared spectroscopy analysis 2.2.1. Instrumentation and collection of spectra Near-infrared spectra were scanned using a Bruker MPA I FT-NIR Analyzer (Bruker, Germany) that measures diffuse reflectance. This spectrometer has different measurement channels. For cork plank spots, a fiber optic reflection probe with an area of spectrum of 0.07 cm2 was used. The spectra were recorded from 12500 to 4000 cm−1 with a resolution of 8 cm−1. In total, 236 scans were averaged per spectrum. For cork granulate samples the integrating sphere was used with an area of spectrum of 35.34 cm2. Spectra were obtained from an average of 64 scans and collected every 8 cm−1 from 12500 to 3600 cm−1. In all cases, the spectra were stored as log (1/Reflectance). The spectrometer was controlled using the OPUS 7.5 software (Bruker, Germany).

2.2.3. Quantitative NIRS analysis (cork granulate anomalies) Quantitative NIRS analysis was conducted using the software Opus Quant (Bruker, 2011a). Calibration was performed using the two sets of 180 average spectra obtained from each of the two sets of samples prepared (yellow stain (G-YS) and corkback (G-CB)). The partial least squares (PLS) method was used to obtain two equations, one for detecting yellow stain and the other for corkback. The equations were validated by means of cross-validation in order to include all the spectral variability of the data set. The maximum number of PLS vectors was set at 10. A manual analysis was performed, testing multiple spectral ranges and pretreatments (standard normal variate, multiple scatter correction, first derivative, second derivative and combinations

2.2.2. Qualitative NIRS analysis (cork plank anomalies) Qualitative NIRS analysis was conducted using Opus Ident software 3

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of them) to determine the best equation for each anomaly. The quality of the NIRS model was defined by means of root mean standard error of cross validation (RMSECV, Eq. (4)), coefficient of determination (R2), the residual prediction deviation (RPD, Eq. (5)) and systematic error or BIAS (Stockl & Oechsner, 2012). N

1 * (x iactual N i=1

RMSECV = xiactual

x ipredicted ) 2

(4) xipredicted

is the true percentage of a sample i and is the predicted percentage for that sample. N is the total number of samples.

RPD =

SD SECV

(5)

Where SD is the standard deviations of the reference data as measured using the conventional method and SECV is the standard error of crossvalidation. An RPD value above 5 is declared as “good to very good” and an RPD value above 10 denotes an “excellent” calibration model (Williams & Sobering, 1993). BIAS allows us to determine whether the calibration equation overestimates or underestimates. In addition, the critical level (LC) (Eq. (6)) and limit of detection (LD) (Eq. (7)) were calculated (Boqué, 2004) to determine only the presence or absence of the anomaly, without taking into account the percentage of yellow stain or corkback:

LC = t1

,v

* s0

LD = 2 * t1

,v

* s 0 = 2 * LC

(6) (7)

T1-α, v is the value of a t-Student distribution for a level of significance α and v degrees of freedom and s0 is the estimation of the standard deviation of the 10 samples with 0 % anomaly for each of the groups (yellow stain or corkback). The best equation for each of the anomalies was selected by taking into account the smallest value of the RMSECV and the lowest value of LC, to obtain a model as accurate as possible and with the greatest discrimination capacity.

Fig. 2. Mean spectra for cork plank spots (above) and cork granulate (below) in the zone with the main absorption peaks.

Sánchez-González, García-Olmo, & Prades, 2015, 2016; Shenk & Westerhaus, 1995; Shenk et al., 2008). It can be observed that the greater lignin content in lignified cork and corkback granulate increases the intensity of the spectrum. Specifically, the intensity of the band at 6890 cm−1 is a good indicator of the presence of lignin (Shenk et al., 2008) which matches with lignified cork and corkback granulate.

3. Results and discussion 3.1. Spectra The spectral library is composed of 686 spectra divided into 2 groups. On the one hand, 326 spectra obtained from 90 cork planks to discriminate between pure cork, yellow stain and lignified cork. On the other hand, 360 spectra obtained from 360 samples of cork granulate with different percentages of yellow stain and corkback. Fig. 2 shows the mean spectra obtained for each type of attribute in cork plank spots and cork granulate in the zone with the main absorption peaks. As far as we know, this is the first time that spectra of samples with different percentages of corkback on cork granulate have been measured. In addition, it is also the first time that spectra of cork plank spots with yellow stain have been measured; since they were only measured on cork granulate in our previous work (Pérez-Terrazas et al., 2018). All spectra showed the characteristic peaks reported in previous studies for cork (Pérez-Terrazas et al., 2018; Prades et al., 2017; Sánchez-González et al., 2016). In the cases of pure cork and yellow stain, spectra of cork granulate showed higher intensity (log (1/R)) than that of cork plank spots, although the highest intensity, between 5300 and 4000 cm−1, corresponded to spectra of lignified cork spots. However, it must be taken into account that different measurement channels for cork (optic probe) and granulate (integrating sphere) were used. Bands 6890 and 5180 cm−1 correspond to the first overtone and combination bands of the −OH group, band 5714 cm-1 corresponds to −CH group in the second and first overtone, band 4650 cm−1 is assigned to the −CH and C]O combination bands and bands around 4300 cm−1 correspond to −CH combination bands of the −CH and −CH2 structures (Prades et al., 2010, 2012; Prades et al., 2014, 2017;

3.2. Qualitative analysis of cork planks The analysis was developed using only part of the NIR region, 12130 - 4096 cm−1, using second derivative (5 smoothing points) and standard normal variate (SNV) preprocessing of the spectra. Using the factorization algorithm, the multivariate spectral data space of the samples (229 classification spectra) was reduced to three orthogonal principal components (PC). Fig. 3 shows the loading for the first, second and third principal components. The PC1 had an important effect on the reproduction of classification spectra with an Eigen value of 2.636 but it did not explain a significant distinction among groups because scores were very similar for all groups (Table 4). PC2 and PC3 only had a minor effect on the reproduction of the classification spectra (Eigen values of 0.359 and 0.005 respectively) but showed high discrimination capacity, giving very different scores for each of the groups (Table 4). The loadings of PC2 and PC3 were most significant between 5400 4100 cm−1 and 6000 - 4100 cm−1 respectively. In all components, the zone between 6500 - 7500 cm−1 also has considerable weight. All the classification spectra were identified correctly so partial classification errors for each group and total classification error were 0.0 % (Table 5). In relation to the validation spectra, all the spectra of yellow stain and lignified cork were identified correctly and only 4 validation spectra of cork were not identified, so the partial validation error of cork was 11.4 %. Although there were 4 not identified cork spectra, the closest group 4

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Fig. 3. Loadings of the principal components (PC1, PC2 and PC3) of qualitative NIRS analysis for cork planks.

selectivity parameter was greater than one in all cases except in the case of lignified cork and yellow stain, which had a selectivity of less than one (S = 0.83) and therefore there is an overlap between groups of cork with anomalies. Fig. 4 shows the scores for classification spectra, all of which were within their sphere. It should be noted that the pure cork sphere does not overlap with any other sphere and is also more compact than the others. This is of particular interest since the most important task is to discriminate between pure cork and cork with anomalies, rather than identifying the specific anomaly present. Therefore, these results show that NIRS allows us to discriminate with a high level of precision between pure cork and cork with yellow stain or lignified cork clearly present; hence it may also be useful to detect anomalies in the production of one piece natural cork stoppers. In addition, it correctly discriminates between the two anomalies studied in the present work, so this study should be extended in the future to other cork anomalies such as earthy cork, which presented the best results in the previous quantitative analysis of anomalies developed by Prades et al. (2017).

Table 4 Scores of Eq. (1) (T1, T2 and T3) from the average spectrum of each group of qualitative NIRS analyses for cork planks. Type

T1

T2

T3

Pure cork (PC) Yellow stain (YS) Lignified cork (LC)

0.609 0.535 0.586

−0.234 0.827 0.510

0.757 −0.172 −0.630

Table 5 Different errors obtained using the qualitative NIRS analysis to identified spectra from spots of pure cork, yellow stain and lignified cork obtained in cork planks.

Classification

Validation

Number of spectra identified Classification error (%) Number of spectra identified Validation error (%)

Pure cork (PC)

Yellow stain (YS)

Lignified cork (LC)

83/83

75/75

71/71

Partial Total

0.0 0.0 31/35

0.0

0.0

33/33

29/29

Partial Total

11.4 4.1

0.0

0.0

3.3. Quantitative equations for cork granulate Table 6 shows the main statistics for the best NIRS calibration equations to predict the percentage of anomalies in cork granulate. The best equation for yellow stain was developed using Standard Normal Variate (SNV) spectrum preprocessing, using only part of the NIR region, 8474 - 4532 cm−1. For corkback granulate, the best equation was developed using two parts of the NIR region, 9403 - 7884 and 6156 3841 cm−1, employing Multiplicative Scatter Correction (MSC) preprocessing of the spectra. In both cases, the region closest to the visible light (12500 - 9400 cm−1) did not improve the accuracy of the models. The RMSECV for yellow stain was 1.50 % with a 10 PLS vector (Fig. 5). R² and RPD for yellow stain detection were 99.62 % and 16.30 respectively. Hence, more than 99 % of the data variability is explained by the equation and the NIRS calibration equation is excellent (RPD above 10) (Williams & Sobering, 1996), the result being much better than that obtained in our previous work, where RMSECV was 2.34 %, R2 was 99.42 % and RPD was 13.10 % (Pérez-Terrazas et al., 2018). The systematic error or BIAS was negative (-0.00038), which indicates that the model slightly overestimates the percentage of yellow stain, thus giving security to the calibration. For corkback granulate, the number of PLS vectors was 3. Although the smallest RMSECV is achieved for 5 PLS vectors (Table 6), the rest of

in all cases was the pure cork group. Misclassification may be due to slight internal anomalies which are not visible on the surface. Total validation error was 4.1 % (Table 5). As far as we know, qualitative NIRS analysis has not been used previously to identify anomalies on cork planks so it is not possible to draw comparisons. As regards quantitative NIRS studies to identify anomalies on cork, only one, specifically on lignified cork (Prades et al., 2017), has been conducted. This study reported a R2 of 0.21 % and RPD of 1.14 % in the determination of lignified cork. The results of our study suggest that a qualitative model may provide a better approach than a quantitative one when developing a NIRS application to detect cork anomalies. In addition, other qualitative NIRS analyses of cork, such as that conducted by Prades et al. (2012) to determine geographical origin in cork samples, provided similar results in terms of classification and validation errors with 1.9–4.2 % and 1.7–1.9 % respectively. In relation to the capacity to discriminate between groups, the 5

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Fig. 4. Scores for all classification spectra for each of the main components. Spheres are the pure cork, cones are the yellow stain and inverted cones are lignified cork. The large spheres indicate the threshold for each group. Table 6 Statistics obtained for the best NIRS calibration equations for yellow stain and corkback. Number of PLS vectors (Rank), root mean square error of estimation (RMSEE), coefficient of determination (R²), residual prediction deviation (RPD), root mean square error of cross validation (RMSECV), systematic error (BIAS), critical level (LC) and limit of detection (LD). SNV: Standard Normal Variate; MSC: Multiplicative Scatter Correction. Anomaly

Yellow stain Corkback

Spectral range (cm−1)

Mathematic treatment

8474 - 4532 9403 - 7884 6156 - 3841

SNV MSC

Rank

10 3

Calibration

Validation

RMSEE (%)

R2 (%)

RPD

RMSECV (%)

R2 (%)

RPD

BIAS

Lc (%)

LD (%)

1.14 2.80

99.80 98.71

22.20 8.80

1.50 2.96

99.62 98.53

16.30 8.25

−0.00038 0.00866

1.49 1.32

2.98 2.63

the statistics are worse. The RMSECV was 2.96 %, so the model also has a high level precision. R2 for corkback was greater than 98 % (98.53 %) and RPD was 8.25. This RPD value above 8 indicates that the model is satisfactory for quality assurance (Williams & Sobering, 1996). BIAS was 0.00866, so the model slightly underestimates the percentage of corkback, which should be taken into account when applying the model. Fig. 6 shows the percentages of both anomalies on cork granulate predicted by the equations versus the real values for each of the samples. For percentages below 10, which are those of most interest given that the amount of yellow stain or corkback present in the production lines is always small, the predicted values were very similar to the real values in both cases and all samples were included in the 95 % prediction limits. As the percentages increase, the accuracy of the equation for yellow stain (top) remains high, with only five samples outside the 95 % prediction limits: one of 15 %, one of 25 %, another of 50 % and two of 100 %. However, in the case of corkback (bottom), the accuracy is slightly worse, especially from 35 % upwards. Table 6 shows the critical level and the limit of detection for a level of significance (α) of 5 %. The standard deviation of the samples without yellow stain and corkback anomalies are 0.8219 and 0.7261 respectively. The critical level for yellow stain was 1.49 %, meaning that, at a confidence level of 95 %, any sample with a predicted value higher than

1.49 % has yellow stain. There is a 5 % probability of a false positive error, that is to say, analysis of a white sample giving a positive for yellow stain. The limit of detection was 2.98 %, so this is the minimum percentage of yellow stain for which we are able to state (at a 95 % confidence level) that the sample is not a white. There is a 5 % probability of a false negative error, so that, analysis of a sample with yellow stain gives a negative for yellow stain. In the case of corkback, the critical level and the limit of detection was 1.32 % and 2.63 % respectively. These results show that NIRS technology allows us to detect different anomalies such as yellow stain and corkback in cork granulate with high discrimination capacity, so it could be useful for quality control in the production lines of the granulate industry. 4. Conclusion Qualitative NIRS analysis allows discrimination between pure cork, lignified cork and yellow stain in cork planks. All the classification spectra and most of the validation spectra are correctly identified. This suggests that NIRS technology may provide a good approach for discriminating other anomalies on cork planks. Quantitative NIRS analysis carried out to determine the percentage of yellow stain and corkback in granulate samples have a R2 of 99.62 - 98.53 % and a RMSECV of 6

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Fig. 5. Root mean standard error of cross validation (RMSECV) versus number of PLS vectors (Rank) for quantitative NIRS models for cork granulate (yellow stain and corkback).

NIRS technology provides a useful tool to detect anomalies on different types of cork sample. This technique can improve quality control procedures both in the manufacturing of cork stoppers and in the bottling lines of wine industry, allowing a better quality level of the whole packaging process. To generalize the results on a broader scale and be able to be used by the industry, it would be necessary to use actual samples, study more cork anomalies and use cork from different geographical origins. CRediT authorship contribution statement David Pérez-Terrazas: Conceptualization, Methodology, Software, Formal analysis, Writing - original draft. José Ramón GonzálezAdrados: Conceptualization, Methodology, Writing - review & editing, Supervision. Mariola Sánchez-González: Conceptualization, Methodology, Writing - review & editing, Supervision. Acknowledgements David Pérez-Terrazas was funded by the Spanish Ministry of Education and Vocational Training (FPU Grant reference FPU17/ 03295). The authors would like to thank the INIA-CIFOR Cork Laboratory assistants María Luisa Cáceres and Lorenzo Ortiz Buiza for all their work in the laboratory, and to Adam Collins for his linguistic assistance. References Anjos, O., Rodrigues, C., Morais, J., & Pereira, H. (2014). Effect of density on the compression behaviour of cork. Materials & Design, 53, 1089–1096. Boqué, R. (2004). El límite de detección de un método analítico [The limit of detection of an analytical method]. Técnicas de Laboratorio, 296, 878–881. Bruker (2011a). User manual: Quant. 161. Bruker (2011b). User manual: Ident. 142. Burns, D. A., & Ciurczak, E. W. (2007). Handbook of near-infrared analysis. New York: Marcel Dekker Inc. Carvalho, F. J. (2009). L’avenir du liege dans le monde [The future of cork in the world] Amorim. Garcia, A. R., Lopes, L. F., De Barros, R. B., & Ilharco, L. M. (2015). The Problem of 2,4,6Trichloroanisole in cork planks studied by attenuated total reflection infrared spectroscopy: Proof of concept. Journal of Agricultural and Food Chemistry, 63(1), 128–135. García-Vallejo, M. C., Varea, S., Cadahía, E., & Fernández-de-Simón, B. (2001). Influencia

Fig. 6. Actual versus predicted values for yellow stain (top) and corkback (bottom). Solid line represents the regression line. Dark shaded region shows the 95 % confidence interval and dashed lines are the upper and lower 95 % prediction intervals of the regression.

1.50–2.96 % respectively. The critical levels for yellow stain and corkback are 1.49 and 1.32 % respectively, so percentages above these can be detected at a 95 % confidence level. These results show that 7

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