文献类型：期刊文献

英文题名：Rapid identification of peanut oil adulteration by near infrared spectroscopy and chemometrics

作者：Peng, Qi[1];Feng, Xinxin[1];Chen, Jialing[1];Meng, Kai[1];Zheng, Huajun[1];Zhang, Lili[1];Chen, Xueping[1];Xie, Guangfa[2]

机构：[1]Shaoxing Univ, Natl Engn Res Ctr Chinese CRW, branch Ctr, Sch Life & Environm Sci, Shaoxing 312000, Zhejiang, Peoples R China;[2]Zhejiang Shuren Univ, Coll Biol & Environm Engn, Key Lab Pollut Exposure & Hlth Intervent Zhejiang, Hangzhou 310015, Zhejiang, Peoples R China

年份：2024

卷号：321

外文期刊名：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

收录：SCI-EXPANDED(收录号：WOS:001260620000001)、、EI(收录号：20242616300750)、Scopus(收录号：2-s2.0-85196548820)、WOS

基金：This work was supported by the Zhejiang Shaoxing Huangjiu Industry Innovation Service Complex- Technological Innovation Project in Zhejiang Province (No.2023KJ082) ; Foundation of Public Projects of Zhejiang Province, China (No.LGN22C200008) ; Program Foundation of Public Projects of Shaoxing city, Zhejiang Province, China (No.2018C30010) ; Foundation of Public Projects of Zhejiang Province, China (No.2017C32101) ; Shaoxing University Fund (No.08021066) , Shaoxing University Fund (No.08220102213) .

语种：英文

外文关键词：Peanut Oil; Near infrared; Qualitative model; Quantitative model; Rapid identification

外文摘要：Peanut oil, prized for its unique taste and nutritional value, grapples with the pressing issue of adulteration by cost-cutting vendors seeking higher profits. In response, we introduce a novel approach using near -infrared spectroscopy to non -invasively and cost-effectively identify adulteration in peanut oil. Our study, analyzing spectral data of both authentic and intentionally adulterated peanut oil, successfully distinguished high -quality pure peanut oil (PPEO) from adulterated oil (AO) through rigorous analysis. By combining near -infrared spectroscopy with factor analysis (FA) and partial least squares regression (PLS), we achieved discriminant accuracies exceeding 92 % (S > 2) and 89 % (S > 1) for FA models 1 and 2, respectively. The PLS model demonstrated strong predictive capabilities, with a prediction coefficient (R-2) surpassing 93.11 and a root mean square error (RMSECV) below 4.43. These results highlight the effectiveness of NIR spectroscopy in confirming the authenticity of peanut oil and detecting adulteration in its composition.