Determination of Total Phenols from Actinidia arguta by Visible and Near-Infrared Diffuse Reflection (VIS/NIR) Spectroscopy

doi:10.7506/spkx1002-6630-201308039

Abstract

Abstract: The objective of this study was to establish a mathematical model for quantitative determination of total phenols of Actinidia arguta using the near-infrared diffuse reflectance (NIR) spectra in the wavelength range of 570–1848 nm. The experiment was divided into three stages to set the model (harvest stage, storage of 12 days, storage of 24 days), and compared with the different modeling methods. The results showed that the partial least squares (PLS) model, with respect to the first order derivatives spectrum D1 lg(1/R) and inverse multiple scatter correction(IMSC)treatment, provided better predictive performance for total phenols in Actinidia arguta. In calibration sample set, the correlation coefficient of cross validation (RCV) was 0.8939, and root-mean-square error of cross-validation (RMSECV) was 11.6734 mg/100 g. In prediction set sample, the correlation coefficient of prediction (RP) was 0.8627, and root-mean-square error of prediction (RMSEP) was 16.7690 mg/100 g. These results confirmed that it is feasible to use the established VIS/NIR spectroscopy model for the nondestructive determination of the total phenols in Actinidia arguta. However, further investigation is needed to improve the precision of the model.

Key words: visible and near infrared diffuse reflection spectroscopy, Actinidia arguta, total phenols

CLC Number:

FU Rao，XIN Guang，LI Shu-qian，ZHANG Bo，LIU Chang-jiang，WANG Bao-gang，FENG Xiao-yuan. Determination of Total Phenols from Actinidia arguta by Visible and Near-Infrared Diffuse Reflection (VIS/NIR) Spectroscopy[J]. FOOD SCIENCE, 2013, 34(8): 186-189.

References

[1] 朴一龙，赵兰花. 软枣猕猴桃研究进展[J].北方园艺，2008（3）：76-78.
[2] 陕西省果树研究所, 山东农学院, 河南省博爱县农林局. 柿[M]. 西安: 陕西农业出版社, 1978: 2-5.
[3] 徐广通, 袁洪福, 陆婉珍. 现代近红外光谱技术及应用进展[J ]. 光谱学与光谱分析, 2000, 20 (2) : 134—142.
[4] LU R, GUYER D E, BEAUDRY R M. Determination of firmness and sugar content of apples using near-infrared diffuse reflectance[J]. Journal of Texture Studies, 2007, 31(6): 615-630.
[5] 赵杰文, 张海东, 刘木华. 利用近红外漫反射光谱技术进行苹果糖度无损检测的研究[J]. 农业工程学报, 2005, 21(3): 162-165.
[6] 刘燕德, 应义斌, 傅霞萍. 近红外漫反射用于检测苹果糖度及有效酸度的研究[J]. 光谱学与光谱分析， 2005, 25(11): 1793-1796.
[7] 孙通, 应义斌, 刘魁武, 胡雷秀. 梨可溶性固形物含量的在线近红外光谱检测[J].光谱学与光谱分析，2008,28（11）:2536-2539.
[8] 纪淑娟, 李东华, 重藤和明. 南果梨糖、酸度近红外光谱无损检测模型建立定标参数的确定[J]. 食品科学, 2008, 29(10): 512-516.
[9] 文建萍, 陈兴苗, 孙旭东, 刘燕德.可见近红外漫反射光谱法测定赣南脐橙的表面色泽[J].安徽农业科学，2007,35 (36) :11805 – 11806.
[10] SUBEDI P P, WALSH K B, OWENS G. Prediction of mango eating quality at harvest using short-wave near infrared spectrometry[J]. Postharvest Biology Technology, 2007, 43(3): 326-334.
[11] 虞佳佳, 何勇, 鲍一丹. 基于光谱技术的芒果糖度酸度无损检测方法研究[J]. 光谱学与光谱分析, 2008, 28(12): 2839-2842.
[12] 张鹏，李江阔，孟宪军，张平，王宝刚，冯晓元. 磨盘柿可溶性固形物的可见/ 近红外漫反射光谱无损检测 [J].食品科学，2011,32(6):191-194.
[13] Sylvie Bureau, David Ruiz, Maryse Reich. Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy [J]. Food Chemistry, 113 (2009) 1323–1328.
[14] McGLONE V A, CLARK C J, JORDAN R B. Comparing density and VNIR methods for predicting quality parameters of yellow-fleshed kiwifruit(Actinidia chinensis)[J]. Postharvest Biology Technology, 2007,46(1): 1-9.
[15] 陈香维, 杨公明. 测试部位对猕猴桃近红外光谱响应特性的影响[J].农业机械学报, 2009, 40(1): 124-128.
[16] 马广,傅霞萍,周莹,应义斌,徐惠荣,谢丽娟,林涛.大白桃糖度的近红外漫反射光谱无损检测试验研究[J].光谱学与光谱析，2007,27(5):907-910.
[17] 陆辉山,傅霞萍,谢丽娟,应义斌.可见/ 近红外光估测完整柑橘水果可溶性固形物含量的研究[J]. 光谱学与光谱析，2007,27(9):1721-1730.
[18] 王加华,陈卓,李振茹,韩东海.洋梨硬度的便携式可见／近红外漫透射检测技术[J]. 农业机械学报，2010,41(11):129-133.
[19] Cayuela J A. Vis/NIR Solids Prediction in Intact Oranges (Citrus Sinensis L) cv. Valencia Late by Reflectance [J].Postharvest Biol. Technol.,2008,47(1):75-80.
[20] Williams P, Norris K H.Vuriable Agriculture and Food Industrics[M]. Second ed. St. Pual,MN:The Association of Cereal Chemist.,2001.171-185.