Abstract TVB-N is one of the important indexes of the freshness of chilled pork. To present the freshness more accurately and completely, a visualization technology of chemical composition was applied and optimized for pork freshness detection. Two hyperspectral cameras covering visible and near-infrared wavebands were used to detect the TVB-N content. GA, SPA, iPLSR, and siPLSR were employed to select characteristic wavebands from the spectra data extracted from the hyperspectral images, and PLSR models were established on full wavebands and characteristic wavebands. According to the models established on the spectral data, TVB-N distribution maps were obtained, and URV and ICV were employed to evaluate the performance of distribution maps. The results showed that, both PLSR models established on the characteristic wavebands and full wavebands achieved good accuracy, and that the characteristic wavebands cluster was in the spectral range of 600-750 nm, 1020-1120 nm, and 1450-1570 nm. To optimize the performance of distribution maps, prediction models were reconstructed under the guidance of URV and ICV. This study provided a method of predicting TVB-N content of pork in both spectral and imaging aspect for online testing the freshness of chilled pork, which improves the efficiency and quality of testing.

Abstract Ginkgo biloba leaf is a kind of food raw material with high concentration of total flavonoid content (TFC). Since conventional measurements of TFC are time-consuming and destructive, the approach of hyperspectral imaging-based prediction was investigated in this study. Hyperspectral images were collected in visual near-infrared (VIS-NIR) and short-wave near-infrared (SW-NIR). Initial comparative analysis on the full spans of the two wavelengths using partial least squares regression (PLSR) and support vector regression (SVR) showed that the TFC of the leaves could be barely captured only when PLSR working on short-wave infrared, with R p 2 and RMSE p on testing set being respectively 0.5496 and 0.6384 mg/g. To further improve prediction performance meeting industrial requirements, another comparative study on feature selection was conducted to fine-tune the PLSR on SW-NIR using a genetic algorithm (GA) and successive projections algorithm (SPA). Results showed that GA-PLSR with 50 characteristic wavelengths mostly from the ranges of 1100-1200 nm and 1400-1500 nm had a significant improvement in performance, giving 0.8482 and 0.2967 mg/g for R p 2 and RMSE p, respectively. Therefore, an approach, GA-PLSR modeling on SW-NIR hyperspectral images, was established for the ginkgo leaves industry to rapidly and non-invasively predict the leaf concentration from the total flavonoid of ginkgo biloba seedling leaf.

Abstract Chemometric modeling concerns both accuracy and computational expense for the prediction of quality-indicating attributes of food materials. Modeling approaches were explored with the hyperspectral images with pH and Brix values of greengages. A two-phase architecture was applied for modeling. Firstly, waveband selection was performed using two approaches, i.e., succession projection algorithm (SPA) and its combination with genetic algorithm (SPA+GA). Secondly, multispectral models based on the two feature sets of wavebands were built via a total of six different modeling methods, i.e., partial least squares regression (PLSR) and extreme learning machine (ELM) in their respective stand-alone versions, their applications combined with genetic algorithm (GA), and their ensemble enhancements with modified Adaboost.RT (MAdaboost.RT). Analysis of accuracy and computational expense showed that supervised feature selection with SPA+GA was superior to unsupervised SPA for better modeling accuracy. MAdaboost.RT-ELM showed high accuracy at low computational expense. ELM models were the better base models than the PLSR ones, for being more randomized and diverse. It indicates that MAdaboost.RT-ELM on SPA is the best choice for a quick test on a newly available dataset, while switching the dimensionality reduction from SPA to SPA+GA may yield more accurate models with added, but well worthy, computational expense.