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Acrylamide (ACR), a neurotoxin with carcinogenic properties that can affect fertility, is commonly found in fried and baked foods such as potato chips. This study was carried out to predict the ACR content in fried and baked potato chips using near-infrared (NIR) spectroscopy. Effective wavenumbers were identified using competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA). Six wavenumbers (12799 cm -1 , 12007 cm -1 , 10944 cm -1 , 10943 cm -1 , 5801 cm -1 , and 4332 cm -1 ) were selected using the ratio (λ i /λ j ) and difference (λ i -λ j ) of any two wavenumbers from the CARS and SPA results. First, partial least squares (PLS) models were established based on full spectral wavebands (12799-4000 cm -1 ), and the prediction models were subsequently redeveloped based on effective wavenumbers to predict ACR content. Results showed that the full and selected wavenumbers-based PLS models obtained the coefficient of determination (R 2 ) of 0.7707 and 0.6670, respectively, and the root mean square errors of prediction (RMSEP) of 53.0442 μg/kg and 64.3810 μg/kg, respectively, in the prediction sets. The results of this work demonstrate the suitability of NIR spectroscopy as a non-destructive method for predicting ACR content in potato chips., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

[Display omitted] • Hyperspectral imaging effectively predicted acrylamide content in roasted coffee. • The visualization images of the acrylamide content in roasted coffee were obtained. • It is helpful for the large-scale detection of acrylamide content in roasted coffee in industries. Since it is carcinogenic to humans, the acrylamide content in heat-processed foods should be detected and controlled. This study investigated the spatial distribution and visualization of the acrylamide content in roasted coffee. First, hyperspectral images were collected, from which the spectral-pixel features were extracted. Besides partial least square (PLS), five wavelengths (888, 1123, 1456, 1636, and 1734 nm) based on the successive projections algorithm (SPA) were used to establish prediction models (multiple linear regression (MLR), CatBoost, and XgBoost). SPA-MLR presented the optimal outcome, with a prediction coefficient of determination (R p 2) and a root mean square error of prediction (RMSEP) of 0.81 and 35.90 μg/kg, respectively. The acrylamide content values for all pixels in the hyperspectral images were subsequently calculated using the prediction equation, thereby producing the spatial distribution and visualization images. The results demonstrated that hyperspectral imaging can effectively predict and visualize the acrylamide levels in roasted coffee. [ABSTRACT FROM AUTHOR]