Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds. (1st February 2022) - Mishra, Gayatri; Panda, Brajesh Kumar; Ramirez, Wilmer Ariza; Jung, Hyewon; Singh, Chandra B.; Lee, Sang-Heon; Lee, Ivan
Abstract: 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. Highlights: 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.