Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging

Fast quantitative analysis and chemical visualization of amylopectin and amylose in sweet potatoes via merging 1D spectra and 2D image

The quantitative analysis and spatial chemical visualization of amylopectin and amylose in different varieties of sweet potatoes were studied by merging spectral and image information. Three-dimensional (3D) hyperspectral images carrying 1D spectra and 2D images of hundreds of the samples (amylopectin, n = 644; amylose, n = 665) in near-infrared (NIR) range of 950-1650 nm (426 wavelengths) were acquired. The NIR spectra were mined to correlate with the values of the two indexes using a linear algorithm, generating a best performance with correlation coefficients and root mean square error of prediction (rP and RMSEP) of 0.983 and 0.847 g/100 mg for amylopectin, and 0.975 and 0.500 g/100 mg for amylose, respectively. Then, 14 % of the wavelengths (60 for amylopectin, 61 for amylopectin) were selected to simplify the prediction with rP and RMSEP of 0.970 and 1.103 g/100 mg for amylopectin, and 0.952 and 0.684 g/100 mg for amylose, respectively, comparable to those of full-wavelength models. By transferring the simplified model to original images, the color chemical maps were created and the differences of the two indexes in spatial distribution were visualized. The integration of NIR spectra and 2D image could be used for the more comprehensive evaluation of amylopectin and amylose concentrations in sweet potatoes.

Study on the Diffusion and Optimization of Sucrose in Gaido Seak Based on Finite Element Analysis and Hyperspectral Imaging Technology

As a traditional Chinese dish cutting technology process, Gaidao artificially create cuts embedded in the food surface by cutting through it with knife, a process that currently plays an important role in the beef marinating process. And different Gaidao processes directly affect the beef marination flavour and marination efficiency. This study is the first to propose the use of Hyperspectral imaging technology (HSI) combined with finite element analysis to investigate the effect of Gaidao process on the quality of marinated beef. The study was carried out by collecting spectral information of beef marinated with different sucrose concentrations and combining various pre-processing methods and algorithms such as PLS, BiPLS, iPLS, and SiPLS to establish a quantitative model of sucrose concentration in beef, and finally optimizing parameters such as the length, position and number of Gaidao by Finite Element Analysis (FEA), which showed that when marinated with 1.0 mol/m³ sucrose solution, the concentration of sucrose in all tissues in the Gaidao steak reached 0.8 mol/m³ and above, which greatly improved the diffusion effect of the marinade. This work provides new ideas and methods to optimize the beef marinade Gaidao process, which has important practical value and research significance.

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

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R2 values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.