Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds

高光谱成像 多光谱图像 校准 VNIR公司 黄曲霉毒素 核(代数) 线性回归 偏最小二乘回归 主成分回归 支持向量机 人工智能 模式识别(心理学) 遥感 生物系统 化学计量学 数学 近红外光谱 化学 计算机科学 统计 机器学习 地质学 组合数学
作者
Gayatri Mishra,Brajesh Kumar Panda,Wilmer Ariza Ramirez,Hye-Won Jung,Chandra B. Singh,Sang-Heon Lee,Ivan Lee
出处
期刊:Lebensmittel-Wissenschaft & Technologie [Elsevier BV]
卷期号:155: 112954-112954 被引量:16
标识
DOI:10.1016/j.lwt.2021.112954
摘要

Almonds are highly susceptible to Aflatoxin B1 (AFB1) contamination, which can result in significant economic losses. Current detection techniques are destructive, time consuming and unfit for in-line application. This study investigated the potential of hyperspectral imaging in the near infrared (NIR) range (900–1700 nm) to develop a rapid and non-destructive protocol for determination of AFB1 content of single almond kernels. Almond kernels were treated to varying AFB1 concentration by artificial infection using standard AFB1 solution and used for Experimentation. Reference AFB1 concentration and their association with the spectral data were modelled using partial least squares regression (PLSR) combined with suitable spectral preprocessing techniques. Superior models were obtained with full-spectrum regression models with R 2 and RMSEP values of 0.958 and 0.089 μg/g, respectively. Competitive-adaptive reweighted sampling (CARS) was used to select the feature wavelengths for rapid quantification of AFB1 at commercial scale. Multiple linear regression (MLR) models were developed using the selected wavelengths for its applicability in multispectral imaging systems. Results showed that the MLR model achieved good prediction capabilities with R 2 and RMSEP values of 0.948 and 0.090 μg/g, respectively. The results demonstrated that hyperspectral imaging had potential for rapid and nondestructive determination of AFB1 concentration in single kernel almonds. • Hyperspectral imaging was used to develop non-destructive method for detection of aflatoxin B1 in almonds. • Calibration models were developed using PLSR and suitable preprocessing techniques. • CARS-PLS was used to select the feature wavelengths for rapid and accurate quantification. • MLR models with feature wavelengths showed good prediction capabilities.
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