近红外光谱技术在葡萄及其制品品质检测中的应用研究进展

doi:10.3964/j.issn.1000-0593(2021)12-3653-07

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Abstract Grape, which is one of the fruits with the largest planting area globally, has rich nutritional value, medicinal value and economic value. According to consumers’ consumption demands and storage and transportation requirements for products, grapes were processed into common grape products, such as raisins, grape juice, wine, and grape seed oil. Based on the growing concerns over the quality and safety of foods and the demands for high-quality fruits and vegetables, how to quickly and effectively evaluate the quality of grapes and grape products has become urgent and imperative. With the development of non-destructive testing technology and equipment, near-infrared (NIR) spectroscopy technology has been gradually applied in quality testing of fruits, vegetables and other agricultural products due to its advantages of rapid, non-destructive, accurate, cost-effective, and convenient for online analysis. Nowadays, domestic and foreign scholars have combined the methods of chemometrics and data processing methods, such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), principal component regression (PCR), partial least squares regression (PLSR), support vector machine (SVM), and neural network (NN), etc., to determine the relationship between the general components (such as sugar, alcohol, acid, etc.) and specific components (such as pigments, tannins, aromatic substances, etc.) of grapes and grape products and effective spectral information non-destructively using NIR technology. The correlation between the content of quality components and spectral information has been explored to establish qualitative and quantitative analysis models for the main quality indicators of grapes and grape products, which provided some technical supports for the development of portable near-infrared inspection equipment for grape and online monitoring system for grape juice and wine brewing process. This review systematically summarized NIR technology's domestic and foreign application status in grapes, wine, grape juice and grape products in the past ten years for the first time, aiming to provide technical reference for the subsequent classification and identification and quality evaluation of grapes and grape products. Studies have shown that NIR technology could achieve multi-component detection and classification identification of grapes' complex physical and chemical components through quantitative and qualitative analysis. The research on the determination of physical and chemical properties and internal quality of grapes had made great progress, and the research and application of monitoring and qualitative identification of wine and grape juice are gradually increasing. They were gradually applied to the analysis of grape products, such as polyphenols and anthocyanins in grape skins, and the monitoring of the nutritional growth status of grapevines and grape leaves. This further confirmed that NIR technology is emerging as an effective detection tool for the quality evaluation of grape and grape products, improving the quality values of grapes and grape products and providing technical support for real-time and efficient production management have a broad range of applications. For the future research, to sense grape information during the process of growth, harvest, and post-harvest production, and realize quality control and on-line monitoring of grape and its products in the whole production process, studies are heading to investigate the correlation between the spectral information reflected by the detection data of different categories and the inherent quality of grapes and grape products, and build a robust prediction model with high accuracy based on the multi-source information fusion technology of vision, volatile, taste, and smell.

Key words：NIR; Non-destructive detection; Model; Grape; Quality

Received: 2020-10-31 Accepted: 2021-01-12

ZTFLH:

S123

Corresponding Authors: HAN Chang-jie E-mail: hcj_627@163.com

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)12-3653-07 OR http://www.gpxygpfx.com/EN/Y2021/V41/I12/3653

[1] ZHAO Shan-shan，LI Min-min，XIAO Ou-li，et al（赵珊珊，李敏敏，肖欧丽，等）. Journal of Food Safety & Quality（食品安全质量检测学报），2020，11（18）：6639.
[2] YUAN Lei-ming，CAI Jian-rong，SUN Li，et al（袁雷明，蔡健荣，孙力，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2017，37（4）：1220.
[3] Larrain M，Guesalaga A R，Agosin E. IEEE Transactions on Instrumentation and Measurement，2008，57（2）：294.
[4] González-Caballero V，Sánchez M T，Fernández-Novales J，et al. Food Analytical Methods，2012，5（6）：1377.
[5] Urraca R，Sanz-Garcia A，Tardaguila J，et al. Journal of the Science of Food and Agriculture，2016，96（9）：3007.
[6] Costa D D，Mesa N F O，Freire M S，et al. Postharvest Biology and Technology，2019，150：166.
[7] XU Feng，FU Dan-dan，WANG Qiao-hua，et al（许锋，付丹丹，王巧华，等）. Food Science（食品科学），2018，39（8）：149.
[8] CHEN Chen，LU Xiao-xiang，ZHANG Peng，et al（陈辰，鲁晓翔，张鹏，等）. Food Science（食品科学），2015，36（20）：109.
[9] Xiao H，Li A，Li M Y，et al. Scientia Horticulturae，2018，233：99.
[10] XIAO Hui，SUN Ke，TU Kang，et al（肖慧，孙柯，屠康，等）. Food Science（食品科学），2019，40（8）：300.
[11] Parpinello G P，Nunziatini G，Rombolà A D，et al. Postharvest Biology and Technology，2013，83：47.
[12] Ferrer-Gallego R，Hernández-Hierro J M，Rivas-Gonzalo J C，et al. Journal of Food Engineering，2013，118（3）：333.
[13] Ferrer-Gallego R，Hernández-Hierro J M，Rivas-Gonzalo J C，et al. Journal of the Science of Food and Agriculture，2013，93（4）：967.
[14] Giovenzana V，Beghi R，Tugnolo A，et al. Computers & Electronics in Agriculture，2018，146：86.
[15] Ferrer-Gallego R，Hernández-Hierro J M，Rivas-Gonzalo J C，et al. LWT—Food Science and Technology，2011，44（4）：847.
[16] Piazzolla F，Amodio M L，Colelli G. Journal of Agricultural Engineering，2017，48（2）：109.
[17] Barnaba F E，Bellincontro A，Mencarelli F. Journal of the Science of Food and Agriculture，2014，94：1071.
[18] Yu J，Zhan J，Huang W. Food Analytical Methods，2017，10（10）：3306.
[19] LI Xia, YU Ya-min, YANG Ji-hong（李霞, 于亚敏，杨继红）. China Brewing(中国酿造)，2018，37(4)：143.
[20] Cozzolino D，Cynkar W U，Dambergs R G，et al. Journal of Agricultural and Food Chemistry，2008，56（17）：7631.
[21] Carames E T S，Alamar P D，Poppi R J，et al. Food Analytical Methods，2017，10（5）：1609.
[22] Jin X Y，Wu S M，Yu W J，et al. Journal of AOAC International，2019，102（4）：1174.
[23] JIA Liu-jun, WANG Jian, ZHANG Hai-hong, et al（贾柳君, 王健，张海红，等）. Food Science and Technology（食品科技），2017，42(5)：273.
[24] Fernández-Novales J，López MI，Sánchez MT，et al. Food Research International，2009，42（2）：285.
[25] Cozzolino D，Dambergs R，Janik L，et al. Journal of Near Infrared Spectroscopy，2006，14（1）：279.
[26] Lorenzo C，Garde-Cerdan T，Pedroza M A，et al. Food Research International，2009，42（9）：1281.
[27] Preys S，Roger J M，Boulet J C. Chemometrics & Intelligent Laboratory Systems，2008，91（1）：28.
[28] Manley M，Van Zyl A，Wolf E E H. South African Journal of Enology and Viticulture，2017，22（2）：93.
[29] Baca-Bocanegra B，Hernández-Hierro JM，Nogales-Bueno J，et al. Talanta，2019，192：353.
[30] Nogales-Bueno J，Baca-Bocanegra B，Rodriguez-Pulido F J，et al. Food Chemistry，2015：172. 559.
[31] Cuq S，Lemetter V，Kleiber D，et al. International Journal of Environmental Analytical Chemistry，2019，8：1.
[32] Giovenzana V，Beghi R，Parisi S，et al. Journal of the Science of Food and Agriculture，2018，98：1935.