Optimal Wavelength Selection for Hyperspectral Imaging Evaluation on Vegetable Soybean Moisture Content during Drying

Abstract

Hyperspectral imaging technology is a promising technique for nondestructive quality evaluation of dried products. In order to realize real-time, online inspection of quality of dried products, it is necessary to determine a few important wavelengths from hyperspectral images for developing a multispectral imaging system. This study presents a binary firework algorithm (BFWA) for selecting the optimal wavelengths from hyperspectral images for moisture evaluation of dried soybean. Hyperspectral images over the spectral region 400–1000 nm, were acquired for 270 dried soybean samples, and mean reflectance was calculated from hyperspectral images for each wavelength. After selecting 12 important wavelengths using BFWA, a moisture prediction model was developed using partial least squares regression (PLSR). The PLSR model with BFWA achieved a prediction accuracy of R p = 0.966 and R M S E P = 5.105 % , which is better than those of successive projections algorithm ( R p = 0.932 and R M S E P = 7.329 % ), and the uninformative viable elimination algorithm ( R p = 0.928 and R M S E P = 7.416 % ). The results obtained by BFWA were more stable, with a smaller standard deviation of R p and R M S E P than those of the genetic algorithm. The BFWA method provides an effective mean for optimal wavelength selection to predict the quality of soybeans during drying.

Prediction of total carotenoids, color, and moisture content of carrot slices during hot air drying using non‐invasive hyperspectral imaging technique

“…An apparent peak in the region between 959.11 to 985.26 was observed in VIP plot for moisture content with the highest peak spikes at 973 nm (Figure 6b and Table 1). The corresponding wavelength was related to moisture content due to the strong absorption of infrared radiation by water and the engagement of hydrogen bonds with one or two OH groups within a NIR spectrum between 730 and 2300 nm which in turn will influence the NIR absorption (Yu et al, 2019; Amodio et al, 2017). This noticeable peak was also associated with the stretching of the H–O–H bonds within water molecules and the prominent absorption peak for water was reported to be around 970 nm (Sturm et al, 2020; Kandpal et al, 2013).…”

Proximate Content Monitoring of Black Soldier Fly Larval (Hermetia illucens) Dry Matter for Feed Material using Short-Wave Infrared Hyperspectral Imaging

“…However, the accuracy of the model can be verified by comparing it with similar experimental results. Yu et al (2019) used a Vis/NIR hyperspectral camera to analyze the moisture content of beans using the PLSR method, with 12 wavelengths and showing Rp=0.966 and RMSEP=5.105%. Huang et al (2014) conducted an experiment to monitor the change in moisture content of beans over drying time using Vis/NIR, showing Rp values of 0.901–0.973 and RMSEP values in the range of 4.6%–9.2%.…”