Nondestructive testing model for main components of potato from different places by near infrared spectroscopy

doi:10.14088/j.cnki.issn0439-8114.2021.12.025

Abstract

Abstract: 36 potato（Solanum tuberosum L.） samples involved in different species and grades were collected from 6 provinces in China. Based on the values determined by chemical methods, near infrared spectroscopy （NIRS） nondestructive testing model for the water, reducing sugar and starch content were constructed by partial least squares regression method, respectively. The results showed that the determination coefficients （R²） of constructed NIR model were 0.88 for water, 0.84 for reducing sugar, and 0.92 for starch. The root mean square error of cross validation （RMSECV） was less than 1, and the ratio of the standard deviation of the verification set to the predicted standard deviation （RPD） was all more than 2.5 in each NIR model. In addition, the results of external validation further showed that the R² for three components which was predicted by each constructed NIR model was more than 0.9, and the relative deviation between the predicted value and the measured value in every component was also very small.

Key words: potato（Solanum tuberosum L.）, water, reducing sugar, starch, near infrared spectroscopy

CLC Number:

TH744

WU Yan-yong, SHI Yan-cai, LYU Xue-juan, QIU Li-hang, LUO Han-min. Nondestructive testing model for main components of potato from different places by near infrared spectroscopy[J]. HUBEI AGRICULTURAL SCIENCES, 2021, 60(12): 128-130.

References

[1] 陈萌山, 王小虎. 中国马铃薯主食产业化发展与展望[J]. 农业经济问题,2015,36(12):4-11.
[2] 王秀丽, 陈萌山. 马铃薯发展历程的回溯与展望[J]. 农业经济问题,2020(5):123-130.
[3] 褚小立, 陈瀑, 李敬岩, 等. 近红外光谱分析技术的最新进展与展望[J]. 分析测试学报 2020,39(10):1181-1188.
[4] BARBON J S, MASTELINI S M, BARBON A P A C, et al. Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy[J]. Information Processing in Agriculture,2020,7:342-354.
[5] WANG X.Near-infrared spectroscopy for food quality evaluation[J]. Evaluation technologies for food quality, 2019: 105-118.
[6] 姚婉清, 彭梦侠, 刘婷. 梅州金柚品种的近红外无损鉴别[J]. 食品研究与开发,2019,40(11):166-169.
[7] WEN C A W, BALLARD J W O. Near-infrared spectroscopy for metabolite quantification and species identification[J]. Ecol Evol,2019,9:1336-1343.
[8] LI C, LI L, WU Y, et al, Apple variety identification using near-infrared spectroscopy[J]. Journal of spectroscopy,2018(10):1-7.
[9] 陈文丽, 王其滨, 路皓翔, 等. 最小角回归结合核极限学习机的近红外光谱对柑橘黄龙病的鉴别[J]. 分析测试学报,2020, 39(10):1267-1273.
[10] FERWANDES J N, DOS S L M B,CHOUIN-CARNEIRO T, et al.Rapid, noninvasive detection of Zika virus in Aedes aegypti mosquitoes by near-infrared spectroscopy[J].Science advances,2018,4(5):eaat0496.
[11] 杨越, 杨留长, 纪晓亮, 等. 近红外光谱法快速测定柴胡提取过程中的药效成分[J]. 分析测试学报,2020,39(11):1311-1319.
[12] CHAMPAGNE E, BONIN M, ROYO A A, et al.Predicting terpene content in dried conifer shoots using near infrared spectroscopy[J]. Journal of near infrared spectroscopy,2020,28(5-6):308-314.
[13] CASSON A, BEGHI R, GIOVENZANA V, et al.Environmental advantages of visible and near infrared spectroscopy for the prediction of intact olive ripeness[J]. Biosystems engineering,2020,189:1-10.
[14] 张良, 赵雪君, 石瑛.马铃薯直链淀粉含量近红外模型的建立[J]. 作物杂志,2014(2):73-76.
[15] 吴晨, 何建国, 刘贵珊, 等. 基于近红外高光谱成像技术的马铃薯干物质含量无损检测[J]. 食品与机械,2014,30(4):133-136,150.
[16] 吴晨, 何建国, 贺晓光, 等. 基于近红外高光谱成像技术的马铃薯淀粉含量无损检测[J]. 河南工业大学学报(自然科学版),2014,35(5):11-16.
[17] 王凡, 李永玉, 彭彦昆, 等. 马铃薯多品质参数可见/近红外光谱无损快速检测[J]. 光谱学与光谱分析,2018,38(12):3736-3742.
[18] 孟庆琰, 何建国, 刘贵珊, 等. 基于近红外光谱技术的马铃薯全粉蛋白质无损检测[J]. 食品科技,2015(40):287-291.
[19] 丁继刚, 韩东海, 李永玉, 等. 基于可见/近红外漫透射光谱的马铃薯黑心病及淀粉含量同时在线无损检测[J]. 光谱学与光谱分析,2020,40(3):1909-1915.
[20] 翁霞, 辛广, 李云霞. 蒽酮比色法测定马铃薯淀粉总糖的条件研究[J]. 食品研究与开发, 2013(17):86-88.