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Non-destructive prediction of the hotness of fresh pepper with a single scan using portable near infrared spectroscopy and a variable selection strategy

文献类型： 外文期刊

作者： Chen, Meng-juan 1 ; Yin, Han-liang 1 ; Liu, Yang 1 ; Wang, Rong-rong 1 ; Jiang, Li-wen 1 ; Li, Pao 1 ;

作者机构： 1.Hunan Agr Univ, Coll Food Sci & Technol, Hunan Prov Key Lab Food Sci & Biotechnol, Changsha 410125, Peoples R China

2.Hunan Acad Agr Sci, Hunan Agr Prod Proc Inst, Changsha 410125, Peoples R China

期刊名称：ANALYTICAL METHODS （ 影响因子：3.532； 五年影响因子：3.244 ）

ISSN： 1759-9660

年卷期： 2022 年 14 卷 2 期

页码：

收录情况： SCI

摘要： There has been no study on using near-infrared spectroscopy (NIRS) to predict the hotness of fresh pepper. This study is aimed at developing a non-destructive and accurate method for determining the hotness of fresh peppers using portable NIRS and the variable selection strategy. Spectra from different locations on samples were obtained non-destructively with a single scan. Quantitative models were established using partial least squares (PLS) with a variable selection method or fusion method. The results showed that near-stalk was the best spectral acquisition location for quantitative analysis. The variable selection strategy allows the selection of targeted characteristic variables and improves the results. A fusion method, namely variable adaptive boosting partial least squares (VABPLS), was selected for optimal prediction of the performance. In the optimized model, the root mean square errors of prediction for the validation set (RMSEPvs) of capsaicin, dihydrocapsaicin and pungency degree were 0.295, 0.143 and 47.770, respectively, while the root mean square errors of prediction for the prediction set (RMSEPps) collected one month later were 0.273, 0.346 and 75.524, respectively.

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