基于近红外光谱的红提维生素C含量、糖度及总酸含量无损检测方法
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摘要
建立了基于近红外光谱技术的红提维生素C(Vc)含量、糖度及总酸含量的快速无损检测方法。采集红提样本的光谱数据,分别应用竞争性自适应重加权算法(CARS)、稳定性竞争自适应重加权采样算法(SCARS)和连续投影算法(SPA)进行一次有效特征波段提取,对比测量上述3项指标,建立相应偏最小二乘回归模型,并在一次有效特征波段的提取的基础上结合SPA进行二次特征波段的提取,建立相应偏最小二乘回归算法(PLSR)模型。结果表明,二次特征波段建立的PLSR模型的校正集与预测集的相关系数与一次特征波段提取建立的PLSR相关系数相比有较大提高,模型的均方根误差均有所减小。根据二次特征波段提取的最优波段点建立的红提Vc含量、糖度、总酸含量的最优PLSR模型的校正集相关系数分别为0.983、0.982和0.976,预测集相关系数分别为0.975、0.980和0.975。本研究利用较少波段建立稳定模型预测Vc、糖度和总酸含量的方法,大大减少了运行时间,可为后续便携式检测仪和在线动态检测研究提供技术支持。
The sugar content and total acidity of red globe grape directly affect the taste and quality of fresh food taste and its by-products. Vitamin C is a necessary nutrient for human beings, and it has become the main index to evaluate the quality of red globe grape. The traditional detection method of red globe grape internal quality is destructive sampling, which is cumbersome and time consuming, and has many drawbacks. In this work, based on near-infrared spectroscopy, the rapid nondestructive detection of red globe grape Vc, sugar content and total acidity was performed. The spectral data of red globe grape samples were collected, and the competitive adaptive reweighed algorithm, stability competitive adaptive reweighed sampling algorithm and successive projection algorithm were respectively applied to extract an effective characteristic band. Then the content of Vc and sugar and the total acidity were measured comparatively, and a corresponding partial least squares regression model was established. SPA was combined to extract a secondary characteristic band on the extraction of an effective characteristic band, and a corresponding PLSR model was established. The results showed that the correlation coefficient between the correction set and the prediction set of the PLSR model established by the secondary characteristic band was higher than that established by the primary characteristic band extraction, and the root mean square error of the model was reduced. The correlation coefficients of correction set and prediction set of the optimal PLSR model for red globe grape Vc, sugar content and total acidity based on the optimal band points extracted from the secondary characteristic band were 0.983, 0.982 and 0.976, respectively, and the correlation coefficients of prediction set were 0.975, 0.980 and 0.975, respectively. This stable model built with fewer bands predicts Vc, sugar content and total acidity, and greatly reduces run time. This model provided a technical support for subsequent portable detector and online dynamic detection research.
The sugar content and total acidity of red globe grape directly affect the taste and quality of fresh food taste and its by-products. Vitamin C is a necessary nutrient for human beings, and it has become the main index to evaluate the quality of red globe grape. The traditional detection method of red globe grape internal quality is destructive sampling, which is cumbersome and time consuming, and has many drawbacks. In this work, based on near-infrared spectroscopy, the rapid nondestructive detection of red globe grape Vc, sugar content and total acidity was performed. The spectral data of red globe grape samples were collected, and the competitive adaptive reweighed algorithm, stability competitive adaptive reweighed sampling algorithm and successive projection algorithm were respectively applied to extract an effective characteristic band. Then the content of Vc and sugar and the total acidity were measured comparatively, and a corresponding partial least squares regression model was established. SPA was combined to extract a secondary characteristic band on the extraction of an effective characteristic band, and a corresponding PLSR model was established. The results showed that the correlation coefficient between the correction set and the prediction set of the PLSR model established by the secondary characteristic band was higher than that established by the primary characteristic band extraction, and the root mean square error of the model was reduced. The correlation coefficients of correction set and prediction set of the optimal PLSR model for red globe grape Vc, sugar content and total acidity based on the optimal band points extracted from the secondary characteristic band were 0.983, 0.982 and 0.976, respectively, and the correlation coefficients of prediction set were 0.975, 0.980 and 0.975, respectively. This stable model built with fewer bands predicts Vc, sugar content and total acidity, and greatly reduces run time. This model provided a technical support for subsequent portable detector and online dynamic detection research.
引文
1 XU Feng,FU Dan-Dan,WANG Qiao-Hua,XIAO Zhuang,WANG Bin.Food Science,2018,39(8):149-154许锋,付丹丹,王巧华,肖壮,王彬.食品科学,2018,39(8):149-154
2 GH/T 1022-2000,Table Grapes.People's Republic of China Supply and Marketing Cooperation Industry Standards 鲜葡萄.中华人民共和国供销合作行业标准.GH/T 1022-2000
3 LI Hong-Qiang,SUN Hong,LI Min-Zan.Transactions of the Chinese Society of Agricultural Engineering,2018,34 (8):269-275 李鸿强,孙红,李民赞.农业工程学报,2018,34(8):269-275
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5 NY/T 2637-2014,Refractometric Method for Determination of Total Soluble Solids in Fruits and Vegetables.The People's Republic of China Agricultural Industry Standard 水果和蔬菜可溶性固形物含量的测定折射仪法.中华人民共和国农业行业标准.NY/T 2637-2014
6 GB/T 12456-2008,Determination of Total Acid in Foods.National Standards of the People's Republic of China 食品中总酸的测定.中华人民共和国国家标准.GB/T 12456-2008
7 HE Jia-Lin,QIAO Chun-Yan,LI Dong-Dong,ZHANG Hai-Hong,DENG Hong,SHAN Qi-Mei,GAO Kun,MA Rui.Food Science,2018,39(6):194-199何嘉琳,乔春燕,李冬冬,张海红,邓鸿,单启梅,高坤,马瑞.食品科学,2018,39(6):194-199
8 Kumar S,McGlone A,Whitworth C.Postharvest Biol.Technol.,2015,100:16-22
9 Liu C,Yang S X,Deng L.J.Food Engineer.,2015,161:16-23
10 WANG Fan,LI Yong-Yu,PENG Yan-Kun,SUN Hong-Wei,LI Long.Chinese J.Anal.Chem.,2018,46(9):1424-1431王凡,李永玉,彭彦昆,孙宏伟,李龙.分析化学,2018,46(9):1424-1431
11 HUANG Yu-Ping,Renfu Lu,QI Chao,CHEN Kun-Jie.Spectroscopy and Spectral Analysis,2018,38(8):2362-2368黄玉萍,Renfu Lu,戚超,陈坤杰.光谱学与光谱分析,2018,38(8):2362-2368
12 GUO Zhi-Ming,HUANG Wen-Qian,PENG Yan-Kun,WANG Xiu,TANG Xiu-Ying.Chinese J.Anal.Chem.,2014,42(4):513-518郭志明,黄文倩,彭彦昆,王秀,汤修映.分析化学,2014,42(4):513-518
13 YANG Jia-Bao,DU Chang-Wen,SHEN Ya-Zhen,ZHOU Jian-Min.Chinese J.Anal.Chem.,2013,41(8):1264-1268杨家宝,杜昌文,申亚珍,周健民.分析化学,2013,41(8):1264-1268
14 LUAN Lian-Jun,CHEN Na,LIU Xue-Song,WU Yong-Jiang.Chinese J.Anal.Chem.,2012,40(4):626-629栾连军,陈娜,刘雪松,吴永江.分析化学,2012,40(4):626-629
15 Sun X,Liu Y,Li Y,Wu M,Zhu D.Postharvest Biol.Technol.,2016,116:80-87
16 Ncama K,Opara U L,Tesfay S Z,Fawole O A,Magwaza L S.J.Food Engineer.,2017,193:86-94
17 XU Hui-Rong,LI Qing-Qing.Transactions of The Chinese Society of Agricultural Machinery,2017,48(9):312-317徐惠荣,李青青.农业机械学报,2017,48(9):312-317
18 FAN Shu-Xiang,HUANG Wen-Qian,GUO Zhi-Ming,ZHANG Bao-Hua,ZHAO Chun-Jiang,QIAN Man.Chinese J.Anal.Chem.,2015,43(2):239-244樊书祥,黄文倩,郭志明,张保华,赵春江,钱曼.分析化学,2015,43(2):239-244
19 LIU Yan-De,CHEN Xing-Miao,SUN Xu-Dong.Spectrosc.Spect.Anal.,2008,28 (10):2318-2320刘燕德,陈兴苗,孙旭东.光谱学与光谱分析,2008,28(10):2318-2320
20 Parpinello G P,Nunziatini G,Rombol A D,Gottardi F,Versari A.Postharvest Biol.Technol.,2013,83(3):47-53
21 Arana I,Jaren C,Arazuri S.J.Near Infrared Spectrosc.,2005,13(6):349-357
22 Baiano A,Terracone C,Peri G.Computers Electron.Agric.,2012,87(87):142-151
23 WU Gui-Fang,HUANG Ling-Xia,HE Yong.Spectrosc.Spect.Anal.,2008,28(9):2090-2093吴桂芳,黄凌霞,何勇.光谱学与光谱分析,2008,28(9):2090-2093
24 WANG Wei,JIANG Hui,LIU Guo-Hai,MEI Cong-Li,JI Yi.Chinese J.Anal.Chem.,2017,45(8):1137-1142王玮,江辉,刘国海,梅从立,吉奕.分析化学,2017,45(8):1137-1142
25 Assis C,Oliveira L S,Sena MM.Food Anal.Methods,2017,11(2):578-588
26 Niu C,Yuan Y,Guo H,Wang X,Wang X,Yue T.RSC Adv.,2017,8(1):222-229
27 Ye S,Wang D,Min S.Chemometr.Intell.Lab.Sys.,2008,91(2):194-199
28 LIU Guo-Hai,XIA Rong-Sheng,JIANG Hui,MEI Cong-Li,HUANG Yong-Hong.Spectrosc.Spect.Anal.,2014,34(8):2094-2097刘国海,夏荣盛,江辉,梅从立,黄永红.光谱学与光谱分析,2014,34(8):2094-2097
29 HUANG Zhuang-Rong,SHA Sha,RONG Zheng-Qin,LIU Hai-Ying,CHEN Jin-Hong,ZHU Shui-Jin.Chinese J.Anal.Chem.,2013,41(6):922-926黄庄荣,沙莎,荣正勤,刘海英,陈进红,祝水金.分析化学,2013,41(6):922-926
30 WANG Jian,WANG Liu-San,WANG Ru-Jing,LU Cui-Ping,HUANG Wei,WANG Yu-Bing.Chin.J.Lumin.,2018,39(12):1785-1791王键,汪六三,王儒敬,鲁翠萍,黄伟,汪玉冰.发光学报,2018,39(12):1785-1791
31 Lin Z D,Wang Y B,Wang R J,Wang L S,Lu C P,Zhang Z Y,Song L T,Liu Y.J.Appl.Spectrosc.,2017,84(3):529-534
32 FU Dan-Dan,WANG Qiao-Hua.Food Science,2016,37(22):173-179付丹丹,王巧华.食品科学,2016,37(22):173-179
2 GH/T 1022-2000,Table Grapes.People's Republic of China Supply and Marketing Cooperation Industry Standards 鲜葡萄.中华人民共和国供销合作行业标准.GH/T 1022-2000
3 LI Hong-Qiang,SUN Hong,LI Min-Zan.Transactions of the Chinese Society of Agricultural Engineering,2018,34 (8):269-275 李鸿强,孙红,李民赞.农业工程学报,2018,34(8):269-275
4 GB 5009.86-2016,Determination of Ascorbic Acid in Food.National Standards of the People's Republic of China 食品中抗坏血酸的测定.中华人民共和国国家标准.GB 5009.86-2016
5 NY/T 2637-2014,Refractometric Method for Determination of Total Soluble Solids in Fruits and Vegetables.The People's Republic of China Agricultural Industry Standard 水果和蔬菜可溶性固形物含量的测定折射仪法.中华人民共和国农业行业标准.NY/T 2637-2014
6 GB/T 12456-2008,Determination of Total Acid in Foods.National Standards of the People's Republic of China 食品中总酸的测定.中华人民共和国国家标准.GB/T 12456-2008
7 HE Jia-Lin,QIAO Chun-Yan,LI Dong-Dong,ZHANG Hai-Hong,DENG Hong,SHAN Qi-Mei,GAO Kun,MA Rui.Food Science,2018,39(6):194-199何嘉琳,乔春燕,李冬冬,张海红,邓鸿,单启梅,高坤,马瑞.食品科学,2018,39(6):194-199
8 Kumar S,McGlone A,Whitworth C.Postharvest Biol.Technol.,2015,100:16-22
9 Liu C,Yang S X,Deng L.J.Food Engineer.,2015,161:16-23
10 WANG Fan,LI Yong-Yu,PENG Yan-Kun,SUN Hong-Wei,LI Long.Chinese J.Anal.Chem.,2018,46(9):1424-1431王凡,李永玉,彭彦昆,孙宏伟,李龙.分析化学,2018,46(9):1424-1431
11 HUANG Yu-Ping,Renfu Lu,QI Chao,CHEN Kun-Jie.Spectroscopy and Spectral Analysis,2018,38(8):2362-2368黄玉萍,Renfu Lu,戚超,陈坤杰.光谱学与光谱分析,2018,38(8):2362-2368
12 GUO Zhi-Ming,HUANG Wen-Qian,PENG Yan-Kun,WANG Xiu,TANG Xiu-Ying.Chinese J.Anal.Chem.,2014,42(4):513-518郭志明,黄文倩,彭彦昆,王秀,汤修映.分析化学,2014,42(4):513-518
13 YANG Jia-Bao,DU Chang-Wen,SHEN Ya-Zhen,ZHOU Jian-Min.Chinese J.Anal.Chem.,2013,41(8):1264-1268杨家宝,杜昌文,申亚珍,周健民.分析化学,2013,41(8):1264-1268
14 LUAN Lian-Jun,CHEN Na,LIU Xue-Song,WU Yong-Jiang.Chinese J.Anal.Chem.,2012,40(4):626-629栾连军,陈娜,刘雪松,吴永江.分析化学,2012,40(4):626-629
15 Sun X,Liu Y,Li Y,Wu M,Zhu D.Postharvest Biol.Technol.,2016,116:80-87
16 Ncama K,Opara U L,Tesfay S Z,Fawole O A,Magwaza L S.J.Food Engineer.,2017,193:86-94
17 XU Hui-Rong,LI Qing-Qing.Transactions of The Chinese Society of Agricultural Machinery,2017,48(9):312-317徐惠荣,李青青.农业机械学报,2017,48(9):312-317
18 FAN Shu-Xiang,HUANG Wen-Qian,GUO Zhi-Ming,ZHANG Bao-Hua,ZHAO Chun-Jiang,QIAN Man.Chinese J.Anal.Chem.,2015,43(2):239-244樊书祥,黄文倩,郭志明,张保华,赵春江,钱曼.分析化学,2015,43(2):239-244
19 LIU Yan-De,CHEN Xing-Miao,SUN Xu-Dong.Spectrosc.Spect.Anal.,2008,28 (10):2318-2320刘燕德,陈兴苗,孙旭东.光谱学与光谱分析,2008,28(10):2318-2320
20 Parpinello G P,Nunziatini G,Rombol A D,Gottardi F,Versari A.Postharvest Biol.Technol.,2013,83(3):47-53
21 Arana I,Jaren C,Arazuri S.J.Near Infrared Spectrosc.,2005,13(6):349-357
22 Baiano A,Terracone C,Peri G.Computers Electron.Agric.,2012,87(87):142-151
23 WU Gui-Fang,HUANG Ling-Xia,HE Yong.Spectrosc.Spect.Anal.,2008,28(9):2090-2093吴桂芳,黄凌霞,何勇.光谱学与光谱分析,2008,28(9):2090-2093
24 WANG Wei,JIANG Hui,LIU Guo-Hai,MEI Cong-Li,JI Yi.Chinese J.Anal.Chem.,2017,45(8):1137-1142王玮,江辉,刘国海,梅从立,吉奕.分析化学,2017,45(8):1137-1142
25 Assis C,Oliveira L S,Sena MM.Food Anal.Methods,2017,11(2):578-588
26 Niu C,Yuan Y,Guo H,Wang X,Wang X,Yue T.RSC Adv.,2017,8(1):222-229
27 Ye S,Wang D,Min S.Chemometr.Intell.Lab.Sys.,2008,91(2):194-199
28 LIU Guo-Hai,XIA Rong-Sheng,JIANG Hui,MEI Cong-Li,HUANG Yong-Hong.Spectrosc.Spect.Anal.,2014,34(8):2094-2097刘国海,夏荣盛,江辉,梅从立,黄永红.光谱学与光谱分析,2014,34(8):2094-2097
29 HUANG Zhuang-Rong,SHA Sha,RONG Zheng-Qin,LIU Hai-Ying,CHEN Jin-Hong,ZHU Shui-Jin.Chinese J.Anal.Chem.,2013,41(6):922-926黄庄荣,沙莎,荣正勤,刘海英,陈进红,祝水金.分析化学,2013,41(6):922-926
30 WANG Jian,WANG Liu-San,WANG Ru-Jing,LU Cui-Ping,HUANG Wei,WANG Yu-Bing.Chin.J.Lumin.,2018,39(12):1785-1791王键,汪六三,王儒敬,鲁翠萍,黄伟,汪玉冰.发光学报,2018,39(12):1785-1791
31 Lin Z D,Wang Y B,Wang R J,Wang L S,Lu C P,Zhang Z Y,Song L T,Liu Y.J.Appl.Spectrosc.,2017,84(3):529-534
32 FU Dan-Dan,WANG Qiao-Hua.Food Science,2016,37(22):173-179付丹丹,王巧华.食品科学,2016,37(22):173-179