At-line quality assurance of deep-fried instant noodles using pilot scale visible-NIR spectroscopy combined with deep-learning algorithms

Deep-fried instant noodles produced in a pilot scale facility were monitored for quality parameters using at-line visible-NIR spectroscopy (380–1650 nm) combined with deep-learning algorithms. To build robust calibrations, a wider range of quality parameters for instant noodles viz., moisture (1.6–11.04%), crude protein (8.34–14.39%), total fat (12.38–26.68%), and total ash (1.18–3.15%) were selected. The original raw spectral data was subjected to different pretreatments (standard normal variate (SNV), detrending (DT), first derivative (FD), multiplicative scattering correction (MSC), among others) before being optimized for wavelength selection feature algorithm by competitive adaptive reweighed sampling (CARS). The calibration models were established using deep-learning algorithms based on conventional partial least squares regression (PLSR) and support vector machine regression (SVMR). In general, the SVMR modeling gave an optimum prediction statistics (pretreatment method, coefficient of determination (R2), root mean square error of prediction (RMSEP), ratio of prediction to deviation (RPD)) for moisture (CARS–SNV–DT, 0.98, 0.32, 6.7), crude protein (CARS–FD, 0.98, 0.18, 8.15), and total fat (CARS–MSC, 0.99, 0.39, 10.15) whereas partial least squares regression (PLSR) gave for total ash (CARS–raw, 0.94, 0.12, 4.34). In particularly for noodle manufacturers, the unification of visible-NIR spectroscopy and deep-learning algorithm is a promising to realize sustainability in quality assurance and control.