Development of a method to evaluate the tenderness of fresh tea leaves based on rapid, in-situ Raman spectroscopy scanning for carotenoids

Journal Pre-proofs Development of a method to evaluate the tenderness of fresh tea leaves based on rapid, in-situ Raman spectroscopy scanning for carotenoids Yingying Zhang, Wanjun Gao, Chuanjian Cui, Zhengzhu Zhang, Lili He, Jinkai Zheng, Ruyan Hou PII: DOI: Reference:

S0308-8146(19)31774-1 https://doi.org/10.1016/j.foodchem.2019.125648 FOCH 125648

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Food Chemistry

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25 January 2019 14 June 2019 3 October 2019

Please cite this article as: Zhang, Y., Gao, W., Cui, C., Zhang, Z., He, L., Zheng, J., Hou, R., Development of a method to evaluate the tenderness of fresh tea leaves based on rapid, in-situ Raman spectroscopy scanning for carotenoids, Food Chemistry (2019), doi: https://doi.org/10.1016/j.foodchem.2019.125648

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Development of a method to evaluate the tenderness of fresh tea leaves based on rapid, in-situ Raman spectroscopy scanning for carotenoids Yingying Zhanga1, Wanjun Gaoa1, Chuanjian Cuia, Zhengzhu Zhanga, Lili Heb, Jinkai Zhengc, Ruyan Houa* a

State Key Laboratory of Tea Plant Biology and Utilization; School of Tea and Food

Science & Technology, Anhui Agricultural University, Hefei, 230036, China; and Anhui Province Key Lab of Analysis and Detection for Food Safety, Hefei, 230022, China; b

Dept. of Food Science, Univ. of Massachusetts, Amherst, MA01003, U.S.A

c

Chinese Academy of Agricultural Sciences Inst. of Agro‐products Processing Science

and Technology, Beijing, 100193, China; * Corresponding author: Ruyan Hou, [email protected], Tel: +86 -551-65786765; ORCID ID: https://orcid.org/0000-0003-4423-694X Yingying Zhang, [email protected]; Wanjun Gao, [email protected]; Chuanjian Cui, [email protected]; Zhengzhu Zhang, [email protected]; Lili He, [email protected]; Jinkai Zheng, [email protected]; 1

Yingying Zhang and Wanjun Gao contributed equally to this work

1

Abstract The tenderness of the fresh tea leaves can affect the quality of tea products. It is important to develop a mechanized, accurate way to evaluate the quality of fresh leaves that avoids the uncertainty of a subjective evaluation. Herein, an in-situ, ultra-rapid Raman microscopy strategy to quantify carotenoids in tea leaves was established. The Raman microscopy of carotenoids distribution in leaves from new branches of 22 representative tea varieties showed that the average carotenoid signals increased from a low level in the bud to a high level in the fourth leaf, which represent different developmental stages. The concentration of carotenoids in the bud to fourth leaf, which were from 69.1 ng mg-1 to 199.5 ng mg-1, respectively. These results demonstrate that Raman imaging can serve as an in-situ, non-destructive and ultra-rapid technology for determining the tenderness of fresh tea leaves and be used in quality control for tea processing. Keywords: Fresh tea leaves; Raman spectroscopy; Carotenoids; Tenderness

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1. Introduction Tea is one of the most popular drinks throughout the world. It is made from the young shoots of the tea plant (Camellia sinensis L.), which is an evergreen shrub or small tree. Traditional tea processing involves numerous steps, which were done by hand for about 3000 years in China (Chen, 1984). The quality of tea is significantly affected by the characteristics of the fresh tea leaves, which are assessed by appearance, aroma by the tea master (different maturity levels of the new shoots of tea are shown in Fig. S1). The price of tea made from a single bud or tender leaves is many times higher than that made from older leaves. In particular, green and black teas made from tender leaves can have distinct aromas, such as the fragrance of flowers, when made into tea infusion. Carotenoids are fat-soluble pigments in fresh tea leaves and are being the aroma precursors determinant for aroma quality (Guo, 1996; Lv & Dong, 2002). Carotenoids are isoprenoid compounds (mostly C40) with polyene chains that may contain up to 15 conjugated double bonds (Lichtenthaler, 1987; Tanaka, Sasaki, & Ohmiya, 2008). There are 17 kinds of carotenoids found in fresh tea leaves, which are divided into two categories, namely xanthophylls and carotenes(Wan, 2003). Among them, lutein accounts for about 85% of the total xanthophylls, and β-carotene accounts for about 90% of the total carotenes(Venkatakrishna, Shyamala, Premachandra, & Cana, 1977). In recent years, mechanization of tea manufacturing has been developed and rapidly spread, resulting in cleaner, quicker and more standard tea processing that better meets national safety requirements. With the introduction of such mechanization, it 3

becomes important to develop a mechanized, accurate way to evaluate the quality of fresh leaves that avoids the uncertainty of a subjective evaluation. Although HPLC, gas chromatography (GC) coupled with MS (GC–MS) and HPLC–MS can be severed as fingerprinting methods for judgement the fresh tea quality, these techniques are times consumed and required operational skills. Therefore, it is necessary to establish a nondestructive, rapid, and objective evaluation method that provides science-based guidance for production. Based on massive data analysis, multi-spectral imaging technique and support vector machine to image texture for the sorting of tea categories could also been used in fresh tea quality control. It is regret there are few reports about these techniques. A near-infrared technology has been reportedly used to detect the water content, total nitrogen content and crude fiber content in fresh tea leaves to establish a prediction model for the quality and grade of fresh tea leaves in our lab (Wang, Zhang, Ning, wei, & Li, 2014). This method requires putting the samples into a rotating cup and manually packing the leaves tightly in order to obtain reproducible results, so it hasn’t been used in an online, mechanized detection system. Raman spectroscopy provides a unique chemical fingerprint of a substance and could be used for quality determination within the production line (Muik, Lendl, Molina-Díaz, Pérez-Villarejo, & Ayora-Cañada, 2004; Williams & Bonawi, 2004; Yang & Ying, 2011). Carotenoids produce a distinct and intense fingerprint under the Raman laser, due to movements in their structure (Oliveira, Castro, Edwards, & Oliveira, 2010; Rimai, Heyde, & Gill, 1973; Rimai, Kilponen, & Gill, 1970; Withnall, Chowdhry, Silver, Edwards, & De Oliveira, 2003 ; Da Silva, Vandenabeele, Edwards, 4

& De Oliveira, 2008; Darvin, Gersonde, Albrecht, Sterry, & Lademann, 2007; Pudney, Gambelli, & Gidley, 2011). Raman spectroscopy has been used to assess the freshness of citrus fruits and the maturity of tomatoes by measuring changes in the intensity of Raman signals assigned to carotenoids (Nekvapil, Brezestean, Barchewitz, Glamuzina, Chis, & Cinta-Pinzaru, 2018; Qin, Chao, & Kim, 2011) The aim of this report was to develop an in-situ, ultra-fast, Raman spectroscopybased assessment of the distribution and concentration of carotenoids in tea leaves. This quantitative method uses the Raman spectra of carotenoids as a marker to determine the quality of fresh tea leaves.

2. Materials and methods 2.1. Sample preparation for Raman spectroscopy studies Traditional Chinese teas use the new shoots of tea plants as the raw material for processing. The new shoots consist of one bud and up to four leaves. New shoots (one bud and three leaves or one bud and four leaves) were randomly picked according to the degree of development from the 22 national improved tea plant varieties grown using the same cultivation practices at the Guohe Experimental Tea Farm at Anhui Agricultural University, Hefei, China (Table 1). All samples were picked at one time. The harvested tea leaves of all varieties were immediately brought back to the lab and placed into quartz tubes with an appropriate amount of water and stored at 0~4 °C until Raman spectroscopic imaging was performed. The fresh tea leaves were selected randomly, rinsed with water, dried (room temperature), and pasted via double-sided 5

tape on glass slides for Raman detection. Standard solutions of two carotenoids, β-carotene and lutein purchased from CaroteNature (Lupsingen, Switzerland), were prepared at a concentration of 100 ng mg1

in dichloromethane. To generate a standard curve, 2 μL of the β-carotene and lutein

standard solution was separately dropped onto gold-plated slides for Raman detection. 2.2. Optimization of Raman spectroscopy parameters A Horiba Jobin Yvon, LabRAM HR Evolution Raman spectrometer with both 785-nm and 532-nm excitation lasers and a 10× objective was used. The spectra of fresh tea leaves were taken for 1 s using 785-nm laser excitation at 1 mW, 2 mW, and 5 mW power and for 1 s using 532-nm excitation at 2 mW, 3 mW and 6 mW power in the 600 to 1600 cm−1 range. Raman spectra of fresh tea leaves were acquired for 6 different acquisition times (0.01, 0.02, 0.05, 0.1, 0.5 and 1 s) with a 532 nm excitation and 3 mW laser power was compared. The average spectrum was based on 30 sample points acquired under each different condition. 2.3. Raman spectra imaging and analysis The settings of the microspectrometer were a 10× microscope objective, 200 μm slit width, 600 lines/mm, and a 0.02 s integration time. In this experiment, the total integration time for each map was 7 minutes. Each spectrum was collected in the spectral range of 600 to 1600 cm−1 through the LabSpec6 Software (Horiba). A silicon wafer with a Raman band at 520.7 cm−1 was used to calibrate the spectrometer. 2D Raman images of the samples were collected using EasyNav technology. The EasyNav technology, through the rough 6

surface topography constructed by autofocus, can be used for fast Raman focusing on the sample before its spectrum is acquired, reducing the effect of the topography of a fresh leaf of tea on the two-dimensional Raman spectrum obtained by scanning and increasing the accuracy of the distribution of the compound in the sample. The scanning area was on the middle of the leaf on the side of the main vein (Fig. S2). The scanning area was about 550 × 550 μm (width × length). Raman mapping was performed by sequentially collecting 400 spectra with a 28.9‐μm‐step size on the surface. The total integration time for a Raman imaging was approximately 7 minutes. Raman spectra and 2D Raman images were obtained from three fresh leaves at five leaf positions (bud through 4th leaf) from three randomly selected tea varieties, namely Zhongcha 302, Tieguanyin and Wancha 91. Then, Raman spectra and 2D Raman images of fresh tea leaves of 22 varieties with one bud and three leaves or one bud and four leaves of different leaf positions were collected.

2.4. Carotenoid quantitation In order to determine the content of carotenoids near the surface of fresh tea leaves, a ratio of 5:3 lutein to β-carotene in fresh tea leaves was used (Feng, Gao, Hou, Hu, Zhang, Wan, et al., 2014). Therefore, the two carotenoid standards were mixed at this ratio and then diluted to different concentrations with dichloromethane. Six carotenoid standard solutions were prepared at the concentrations of lutein 200, 100, 50, 25, 12.5, 5 ng mg-1 and β-carotene 120, 60, 30, 15, 7.5, 3 ng mg-1. The carotenoid standard solution (1 μL) was drawn on a silica gel GF254 plate (Anhui Liangchen 7

Silicon Material Co. Ltd, Huoshan, China) with a spotting capillary. The silica gel on the GF254 plate is 250 μm, which is approximately the same as that of the fresh tea leaf, and the silica gel was verified to have no Raman response. The carotenoid standards, distributed in a circle on the silica gel plate were scanned through two random diameters of the sample in 200 scanning points, with each step of the scanning about 10 to 20 μm. The integration of the 400 average signal area data points was done by the LabSpec6 software. The standard curve was drawn using the 400 integrated average signal areas of the Raman spectra plotted against the concentration of the carotenoid mixture. Main carotenoids in fresh tea leaves were conducted by HPLC as our previously described (Feng, et al., 2014) . 2.5. Raman data acquisition and processing Data acquisition and processing were achieved using the LabSpec6 softwareTM (Horiba Jobin Yvon), while TQ analystTM (Thermo Fisher Scientific) was used to analyze spectral data including calculating the peak area of the 1520 cm-1 peak and Principal Component Analysis (PCA) of the Raman spectra. To investigate spectral changes and trends between the experimental groups, PCA was performed in the spectral range of 600 to 1600 cm−1.

3. Results and discussion 3.1. Optimization of Raman parameters Three parameters, namely laser wavelength, intensity of the incident laser, and acquisition time, were tested across different ranges in order to choose the best settings. 8

The intensity of Raman signals of the same tea leaf under 785- and 532-nm laser excitations differed (Fig. S3a). The intensity of the Raman signals were weak under the 785-nm laser, but were relatively strong under the 532-nm laser. The signal intensity increased with increased laser power (2-6 mW; Fig. S3a). Although the Raman spectra was best under 6 mW, the focus on the sample changed in the micro-image because of dehydration of the tea leaf. Therefore, 3 mW power to the 532-nm laser was selected for both good signal-to-noise ratio and accurate focus. Different scan times (0.01-1 s) were also tested (Fig. S3b). The signal was not very clear under an acquisition time of 0.01 s, so the shortest time required for obtaining a clear image, 0.02 s, was selected. Together, these experiments showed that the optimized Raman microspectroscopy settings were using the 532-nm laser at 3 mW power with a 0.02 s acquisition time. 3.2. Raman spectroscopy of carotenoids The Raman spectra obtained from the whole tea leaves contained peaks characteristic of carotenoids, based on a library search and previous studies (Darvin, Sterry, Lademann, & Vergou, 2011; Nekvapil, Brezestean, Barchewitz, Glamuzina, Chis, & Cinta-Pinzaru, 2018; Yang, Wang, Zhao, Tian, Zhang, Xiao, et al., 2017). The characteristics of the compounds were further confirmed by comparing to two carotenoid standards, β-carotene and lutein, which are the carotenoids present in the highest proportion in fresh tea leaves. Comparison of the spectra of fresh leaves with those of the two standards (Fig. S3c) clearly showed spectral differences in both relative intensities and Raman shifts. The major Raman peaks located at 1003, 1156, 1190, and 1520 cm−1 belong to β-carotene, the major Raman peaks located at 1002, 1154, 1187, 9

and 1518 cm−1 belong to lutein, while the major Raman peaks of tea leaves located at 1001, 1154, 1185, and 1520 cm−1 were assigned to in-plane rocking of CH3, polyene C-C stretching vibrations, C-C polyene chain stretching, and polyene C=C stretching vibrations. Slight shifts (≤5 cm-1) were observed for several peaks between lutein and β-carotene. For β-carotene, the peak intensity of the C-C stretching vibration band at 1156 cm-1 was higher than that of the C=C stretching vibration at 1520 cm-1, while for lutein the response intensity of the C-C stretching vibration band at 1154 cm-1 was lower than that of the C=C stretching vibration at 1518 cm-1. These intensity differences may be because lutein is an oxygenated carotene, and the oxidized hydroxyl group may affect the C-C or C=C vibrations on the ring and adjacent positions. Most importantly, this result indicated that Raman spectroscopy can be applied to discriminate different kinds of carotenoids. Moreover, the Raman peak intensity is much higher for β-carotene than for lutein when the same concentration of standard solution was dropped on the gold slide. Comparison of the Raman shift and response intensity of fresh tea leaves and the two carotenoid bands showed that the spectra of the fresh leaves were more similar to that of lutein. This may be because the Raman signal of the C-C polyene stretching in the ring of lutein was sensitive to oxidization of CH to C–OH and because lutein is the main compound (more than 50% of the carotenoids) in fresh tea leaf(Feng, et al., 2014). 3.3. Repeatability of samples Raman spectra and 2D Raman images were obtained from three fresh leaves at five leaf positions (bud through 4th leaf) from three randomly selected tea varieties, 10

namely Zhongcha 302, Tieguanyin and Wancha 91. In images of leaves from one variety, Zhongcha 302, the Raman signal intensity is represented as blue (weak) to yellow (strong) (Fig. 1a). The peak intensity is low in the bud and increases as the leaves age, with the greatest intensity in the 4th leaf. There were no significant differences between the Raman signals obtained from leaves sampled from the same position from different shoots (Fig. 1a, 1b). Comparison of the responses of a characteristic carotenoid peak (at 1520 cm-1) in fresh tea leaves sampled from three representative varieties (Wancha 91, Tieguanyin and Zhongcha 302) revealed that, within a variety, there were no significant differences in the peak area obtained from leaves of the same position (Fig. 1c and Fig. S4). 3.4. Raman imaging of fresh tea leaves from 22 tea varieties National tea varieties improve the yield and quality and enhance the resistance. The fresh tea leaves of these 22 varieties were picked at the same time. Because the maturity of new shoots of different varieties was different, some were a bud and three leaves and others were a bud and four leaves (Table 1, Fig. S5). In-situ Raman spectra were scanned from buds and first, second, third and fourth leaves harvested from 22 tea varieties (Fig. 2). The mapping data are shown for six randomly selected varieties to illustrate the differences in the Raman intensity of carotenoids in fresh tea leaves harvested from different leaf positions. The Raman images of one bud and three leaves of the varieties Anhui 3 hao, Fuzao 2 hao and Mingshan Baihao 131 and of one bud and four leaves of the varieties Shuchazao, Wancha 91 and Echa 5 hao showed that there are distinct differences in the Raman responses. As the leaves from one variety mature, 11

the signal intensity increases, although there is not much increase possible from the third to the fourth leaves (Fig. 2a). One the other hand, leaves of different varieties but from the same leaf position produced Raman images with varied intensities. To clearly show the trend in carotenoid levels in leaves as they age, the area of the carotenoid peak at 1520 cm-1 was compared for fresh leaves from 22 varieties (Fig. 2b). The results showed the same increasing trend with leaf maturity in all varieties. The peak areas of the signals in the leaves from these varieties ranged from 332 to 1833 from bud to fourth leaf, respectively. The full spectra from 600 to 1600 cm-1 from the bud to third leaf from one randomly selected variety, Mingshan Baihao 131, was subjected to PCA (Fig. 2c). In the PCA score plots, most of the Raman spectra of fresh tea leaves of the four different leaf positions are clustered separately. Plotting of PC1 versus PC2 or PC3 indicated that there were significant differences between the fresh leaves taken from different positions. Only a few overlap spectra might come from the uneven distribution of carotenoids on these leaves. 3.5. Quantitative analysis of carotenoids in fresh tea leaves Mixtures of carotenoid standards at different concentrations were dropped onto silica gel GF254 plates and subjected to microscopy and 2D Raman spectroscopy (Fig. 3a). No Raman signal was detected when the mixture contained 5 ng mg-1 lutein and 3 ng mg-1 β-carotene (image not shown). Above a total carotenoid concentration of 20 ng mg-1, the characteristic carotenoid peaks were visible. In the 2D Raman images, the 1520 cm-1 signal intensity increased with the concentration of the mixture. A standard curve was generated using the known mass of the carotenoids present per square 12

millimeter as the abscissa and the peak area (1520 cm-1 peak) of the carotenoid as the ordinate (Fig. 3b; inset). The limit of quantification was calculated as 3.50 ng·mm-2. The results showed that there was a good linear relationship between the Raman response intensity and the mass of the carotenoids. The calibration equation was calculated as y= 27.275x + 31.906, with a correlation coefficient of R2=0.9985. Combining the standard curve and 2D Raman fast scans of fresh tea leaves from the 22 tea varieties, the unit content of each fresh leaf was calculated (Fig. 3c). The presented range represents the content calculated for different leaves for each sample type. The amount of carotenoids in fresh tea leaves was calculated (Table 2). The surface area (0.7 × leaf length × leaf width) and weight of each bud or leaf was determined. The concentration of carotenoids in each bud / leaf ranged from 69.1 ng mg-1 to 199.5 ng mg-1 (Table 2). These results again showed that the carotenoid content increased with leaf maturity. The maximum amount of carotenoids per leaf was about 71 μg, in the 4th leaves, while the minimum content was about 1.6 μg in buds. The amount of carotenoids detected by the in-situ Raman technique and high performance liquid chromatography were compared. Between the two techniques, the contents of carotenoids were similar and showed the same trend with leaf maturity. 3.6. Application of Raman microspectroscopy for grading fresh tea leaves When processing tea, the freshly picked tea leaves are often divided according to their quality, especially for green and black teas. Single bud, one bud and one leaf, one bud and two leaves, one bud and three leaves or one bud and four leaves are classified differently. When fresh leaves are picked, they need to be evaluated more objectively 13

to investigate the possibility of using Raman spectroscopy method detailed above to classify fresh tea shoots, the area of the characteristic carotenoid peak (at 1520 cm-1) was calculated as an average of fresh tea leaves of the harvested shoots for each of the 22 varieties (Table 3). The harvested shoots were pre-graded based on the maturity of fresh tea leaves. The results showed that the Raman response of carotenoids in fresh tea leaves of all grades were different, they did increase with maturity.

4. Conclusions In-situ, ultra-rapid Raman spectroscopy was used non-destructively to determine the Raman signal of leaves of different positions in 22 national tea varieties. The optimized Raman spectroscopy conditions were 532-nm laser excitation with 3 mW laser power. The Raman intensity and content of carotenoids were found to gradually increase with the maturity of the fresh tea leaves. Raman spectra of carotenoids can be as a marker to determine the tenderness of fresh tea leaves. This quantitative method provides a novel simple, in-situ or on-site strategy for investigating the distribution of carotenoids in fresh tea leaves. It can be further used in practical applications as a mechanized, accurate way to rapidly classify raw tea materials into different grades that avoids the uncertainty of a subjective evaluation. Competing interests The authors declare no competing interests. Acknowledgements This work was supported by the National Natural Scientific Foundation of China 14

(No. 31772076 and No. 31270728), Anhui Provinical Key Program for Research and Development (1804b06020349), the National Key Research & Development Program (2016YFD0200900) of China, the Earmarked fund for China Agriculture Research System (CARS-19).

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References Chen, C. (1984). Theory of tea making technology. Shanghai: Shanghai Science and Technology Press. Da Silva, C. E., Vandenabeele, P., Edwards, H. G., & De Oliveira, L. F. (2008). NIRFT-Raman spectroscopic analytical characterization of the fruits, seeds, and phytotherapeutic oils from rosehips. Anal Bioanal Chem, 392(7-8), 1489-1496. Darvin, M. E., Gersonde, I., Albrecht, H., Sterry, W., & Lademann, J. (2007). Resonance Raman spectroscopy for the detection of carotenoids in foodstuffs. Influence of the nutrition on the antioxidative potential of the skin. Laser Physics Letters, 4(6), 452-456. Darvin, M. E., Sterry, W., Lademann, J., & Vergou, T. (2011). The Role of Carotenoids in Human Skin. Molecules, 16(12), 10491-10506. Feng, L., Gao, M. J., Hou, R. Y., Hu, X. Y., Zhang, L., Wan, X. C., & Wei, S. (2014). Determination of quality constituents in the young leaves of albino tea cultivars. Food Chem, 155, 98-104. Guo, W. F. (1996). Study on the formation mechanism of tea aroma. Chinese tea processing(4), 34-37. Lichtenthaler, H. K. (1987). Chlorophylls and carotenoids: Pigments of photosynthetic biomembranes. Methods in Enzymology, 148(1), 350-382. Lv, L. M., & Dong, S. S. (2002). Research progress of tea aroma. Tea 28(4), 181-184. Muik, B., Lendl, B., Molina-Díaz, A., Pérez-Villarejo, L., & Ayora-Cañada, M. J. (2004). Determination of oil and water content in olive pomace using near 16

infrared and Raman spectrometry. A comparative study. Analytical & Bioanalytical Chemistry, 379(1), 35-41. Nekvapil, F., Brezestean, I., Barchewitz, D., Glamuzina, B., Chis, V., & Cinta-Pinzaru, S. (2018). Citrus fruits freshness assessment using Raman spectroscopy. Food Chem, 242, 560-567. Oliveira, V. E. D., Castro, H. V., Edwards, H. G. M., & Oliveira, L. F. C. D. (2010). Carotenes and carotenoids in natural biological samples: a Raman spectroscopic analysis. Journal of Raman Spectroscopy, 41(6), 642-650. Pudney, P. D. A., Gambelli, L., & Gidley, M. J. (2011). Confocal Raman Microspectroscopic Study of the Molecular Status of Carotenoids in Tomato Fruits and Foods. Applied Spectroscopy, 65(2), 127-134. Qin, J. W., Chao, K. L., & Kim, M. S. (2011). Investigation of Raman chemical imaging for detection of lycopene changes in tomatoes during postharvest ripening. Journal of Food Engineering, 107(3-4), 277-288. Rimai, L., Heyde, M. E., & Gill, D. (1973). Vibrational spectra of some carotenoids and related linear polyenes. A Raman spectroscopic study. Journal of the American Chemical Society, 95(14), 4493-4501. Rimai, L., Kilponen, R. G., & Gill, D. (1970). Excitation profiles of laser Raman spectra in the resonance region of two carotenoid pigments in solution. Journal of the American Chemical Society, 92(12), 3824-3825. Tanaka, Y., Sasaki, N., & Ohmiya, A. (2008). Biosynthesis of plant pigments: anthocyanins, betalains and carotenoids. Plant J, 54(4), 733-749. 17

Venkatakrishna, Shyamala, Premachandra, B. R., & Cana, H. R. (1977). Distribution of carotenoid pigments in tea leaves. Tea Quaterly, 47(3), 28-31. Wan, X. C. (2003). Tea biochemistry. Beijing: China Agricultural Press. Wang, M., Zhang, Z. Z., Ning, J. M., wei, L. D., & Li, L. Q. (2014). Study on quality analysis and class rapid evaluation of tea leaf materials based on near infrared technology. Science and Technology of Food Industry, 35(22), 57-60. Williams, J., & Bonawi, W. (2004). Online quality control with Raman spectroscopy in pharmaceutical tablet manufacturing. Journal of Manufacturing Systems, 23(4), 299-308. Withnall, R., Chowdhry, B. Z., Silver, J., Edwards, H. G., & De Oliveira, L. F. (2003). Raman spectra of carotenoids in natural products. Spectrochim Acta A Mol Biomol Spectrosc, 59(10), 2207-2212. Yang, Y., Wang, X. H., Zhao, C. Y., Tian, G. F., Zhang, H., Xiao, H., He, L. L., & Zheng, J. K. (2017). Chemical Mapping of Essential Oils, Flavonoids and Carotenoids in Citrus Peels by Raman Microscopy. J Food Sci, 82(12), 28402846. Yang, D. T., & Ying, Y. B. (2011). Applications of Raman Spectroscopy in Agricultural Products and Food Analysis: A Review. Applied Spectroscopy Reviews, 46(7), 539-560.

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Figure captions Fig. 1 Raman spectra of carotenoids within leaves harvested from the same position and from the same variety. a 2D Raman

images of carotenoids in leaves of Zhongcha

302 (ZC302). Three leaves were harvested for each position from three different shoots. Scale bars: 200 µm. b Raman fast scan spectra of a. c Peak areas of a characteristic carotenoid peak (1520 cm-1) obtained from three fresh tea leaves from five positions harvested from three tea varieties, namely Wancha 91 (WC91; left), Tieguanyin (TGY; middle), and ZC302 (right).

Fig. 2 In situ rapid scanning of Raman spectra of fresh leaves from different tea varieties. a. 2D Raman images of leaves harvested from different positions on shoots, namely one bud and three leaves (left) or one bud and four leaves (right). Only one bud and three leaves were harvested from the varieties on the left because the growth degree of new shoot is different when picking. b. Average peak area was calculated for the carotenoid peak at 1520 cm-1 using the rapid scan data from leaves harvested from different positions on shoots, namely one bud and three leaves (left; for 9 varieties) or one bud and four leaves (right; for 13 varieties) of 22 tea varieties. c. Principal Component Analysis (PCA) of the 1520 cm-1 peak area obtained from fresh leaves harvested from different positions on shoots of the variety MSBH131. Scale bars: 200 µm.

Fig. 3 Quantitative analysis of carotenoids in fresh tea leaves. a. Microscopic images 19

(top) and 2D Raman images (bottom) of carotenoid standards at various concentrations spotted onto silica gel GF254 plates. The Raman intensity scale is shown on the right of each image. b. Raman spectra of carotenoids at various concentrations spotted onto silica gel GF254 plates. The inset is the standard curve of the carotenoid standard solution calculated from the 1520 cm-1 peak. c. The unit content of carotenoids (ng·mm2)

in leaves from different positions [one bud and three leaves (left) or one bud and four

leaves (right)] from 22 tea varieties. Scale bars: 500 µm.

Table 1 Twenty-two tea varieties used in this study

20

Variety

Origin

Potential Use

Growth Habit

An Hui 3 Hao （AH-3）

Anhui, China

green tea, black tea

bush

Fu Zao 2 Hao （FZ-2）

Anhui, China

green tea, black tea

bush

Wan Cha 91 （WC91）

Anhui, China

green tea, black tea

bush

Su Cha Zao （SCZ）

Anhui, China

green tea

bush

Dan Gui （DG）

Fujian, China

green tea, black tea, oolong tea

bush

Huang Jin Gui （HJG）

Fujian, China

green tea, black tea, oolong tea

small tree

Fu Ding Da Bai Cha （FDDBC）

Fujian, China

green tea, black tea, white tea

small tree

Zheng He Dai Bai Cha （ZHDBC）

Fujian, China

green tea, black tea, white tea

small tree

Tie Guan Yin （TGY）

Fujian, China

green tea, oolong tea

bush

Long Jing 43 （LJ43）

Zhejiang, China

green tea

bush

Long Jing Chang Ye （LJCY）

Zhejiang, China

green tea

bush

Zhong Cha 102 （ZC102）

Zhejiang, China

green tea

bush

Zhong Cha 108 （ZC108）

Zhejiang, China

green tea

bush

Zhong Cha 302 （ZC302）

Zhejiang, China

green tea

bush

Zhe Nong 113 （ZN113）

Zhejiang, China

green tea

small tree

Zhe Nong 117 （ZN117）

Zhejiang, China

green tea, black tea

small tree

Ying Shuang

（YS）

Zhejiang, China

green tea, black tea

small tree

Ju Hua Chun （JHC）

Zhejiang, China

green tea, black tea

bush

Yao Shan Xiu Lv （YSXL）

Guangxi, China

green tea

bush

Gui Lv 1 Hao （GL-1）

Guangxi, China

green tea, black tea, oolong tea

bush

Ming Shan Bai Hao 131 （MSBH131）

Sichuan, China

green tea

bush

E Cha 5 Hao （EC-5）

Hubei, China

green tea

bush

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Table 2 Carotenoid content in fresh tea leaves harvested from different positions on the shoot. Leaf position

Leaf surface 2 area（mm ）

Leaf weight （mg）

Unit content -2 （ng mm ）

Content （μg/leaf ）

content（RS）* （ng mg-1）

Content（HPLC）** （ng mg-1）

bud

98

23.7

16.7±0.4

1.6

69.1±1.9

47.7±2.1

first leaf

388.5

95.6

25.0±1.3

9.7±0.5

101.5±5.4

70.2±4.2

second leaf

537.6

148.2

34.1±2.6

18.3±1.4

123.8±9.3

103.3±12.3

third leaf

1062.6

278.0

46.5±5.3

49.4±5.6

177.6±20.2

146.3±14.6

fourth leaf

1398.6

373.6

50.43±5.7

70.5±8.0

199.5±22.5

151.1±9.5

* Carotenoid content calculated from Raman spectra ** Carotenoid content determined with High performance liquid chromatography

22

Table 3 The calculated average Raman intensities of fresh tea leaves from 22 varieties. (Average of Raman intensities from 400 scanning points on one leaf) Raman intensiteis Varieties

bud

one bud and one leaf

one bud and two leaves

one bud and three leaves

one bud and four leaves

AH-3

532±71

697±73

790±75

1055±123

——

FZ-2

591±74

774±101

984±124

1325±162

——

MSBH131

384±47

586±46

692±57

973±123

——

DG

563±75

676±52

776±64

976±120

——

ZHDBC

470±23

544±50

722±93

951±148

——

ZC102

582±45

885±81

912±76

1243±137

——

JHC

561±43

864±111

1090±168

1260±171

——

ZN113

550±71

623±52

764±49

1020±128

——

ZN117

485±39

680±59

888±88

1172±152

——

SCZ

332±18

618±62

716±67

953±126

1162±138

WC91

444±37

593±56

683±60

927±124

1106±170

EC-5

614±61

756±115

862±90

1070±117

1202±148

FDDBC

602±43

726±56

855±79

1034±134

1142±144

HJG

490±40

821±102

951±105

1102±142

1157±169

TGY

440±30

649±35

713±67

928±117

1010±134

GL-1

377±10

421±25

560±52

662±27

880±93

YSXL

548±37

725±36

1087±118

1254±167

1431±138

ZC108

350±32

785±68

1069±103

1363±147

1552±174

LJ43

487±46

590±58

899±104

1075±158

1169±174

LJCY

486±57

573±48

660±74

931±101

1041±140

YS

481±43

605±50

781±75

1213±149

1405±178

ZC302

367

495±56

641±54

948±104

1144±142

23

Fig. 1

Fig. 2

Fig. 3

24

TOC Highlights Development of a method to evaluate the tenderness of fresh tea leaves based on rapid, in-situ Raman spectroscopy scanning for carotenoids 1. An in-situ，rapid quantitative method of carotenoids in tea leaves was established. 2. The amount of carotenoid in the same position of leaves have no obviously difference. 3.The average carotenoid signal increased in tea leaves with the higher grade of maturity. 4. The amount of carotenoids was a good indicator of the tenderness of fresh tea leaves. Declarations of interest: none

25