Fourier transform near-infrared spectroscopy coupled with variable selection methods for fast determination of salmon fillets storage time

An effective deep learning fusion method for predicting the TVB-N and TVC contents of chicken breasts using dual hyperspectral imaging systems

Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are important freshness indicators of meat. Hyperspectral imaging combined with chemometrics has been proven to be effective in meat detection. However, a challenge with chemometrics is the lack of a universally applicable processing combination, requiring trial-and-error experiments with different datasets. This study proposes an end-to-end deep learning model, pyramid attention features fusion model (PAFFM), integrating CNN, attention mechanism and pyramid structure. PAFFM fuses the raw visible and near-infrared range (VNIR) and shortwave near-infrared range (SWIR) spectral data for predicting TVB-N and TVC in chicken breasts. Compared with the CNN and chemometric models, PAFFM obtains excellent results without a complicated processing combinatorial optimization process. Important wavelengths that contributed significantly to PAFFM performance are visualized and interpreted. This study offers valuable references and technical support for the market application of spectral detection, benefiting related research and practical fields.

Application of adaptive chaotic dung beetle optimization algorithm to near-infrared spectral model transfer

A new transfer approach was proposed to share calibration models of the hexamethylenetetramine-acetic acid solution for studying hexamethylenetetramine concentration values across different near-infrared (NIR) spectrometers. This approach combines Savitzky-Golay first derivative (S_G_1) and orthogonal signal correction (OSC) preprocessing, along with feature variable optimization using an adaptive chaotic dung beetle optimization (ACDBO) algorithm. The ACDBO algorithm employs tent chaotic mapping and a nonlinear decreasing strategy, enhancing the balance between global and local search capabilities and increasing population diversity to address limitations observed in traditional dung beetle optimization (DBO). Validated using the CEC-2017 benchmark functions, the ACDBO algorithm demonstrated superior convergence speed, accuracy, and stability. In the context of a partial least squares (PLS) regression model for transferring hexamethylenetetramine-acetic acid solutions using NIR spectroscopy, the ACDBO algorithm excelled over alternative methods such as uninformative variable elimination, competitive adaptive reweighted sampling, cuckoo search, grey wolf optimizer, differential evolution, and DBO in efficiency, accuracy of feature variable selection, and enhancement of model predictive performance. The algorithm attained outstanding metrics, including a determination coefficient for the calibration set (Rc2) of 0.99999, a root mean square error for the calibration set (RMSEC) of 0.00195%, a determination coefficient for the validation set (Rv2) of 0.99643, a root mean squared error for the validation set (RMSEV) of 0.03818%, residual predictive deviation (RPD) of 16.72574. Compared to existing OSC, slope and bias correction (S/B), direct standardization (DS), and piecewise direct standardization (PDS) model transfer methods, the novel strategy enhances the accuracy and robustness of model predictions. It eliminates irrelevant background information about the hexamethylenetetramine concentration, thereby minimizing the spectral discrepancies across different instruments. As a result, this approach yields a determination coefficient for the prediction set (Rp2) of 0.96228, a root mean squared error for the prediction set (RMSEP) of 0.12462%, and a relative error rate (RER) of 17.62331, respectively. These figures closely follow those obtained using DS and PDS, which recorded Rp2, RMSEP, and RER values of 0.97505, 0.10135%, 21.67030, and 0.98311, 0.08339%, 26.33552, respectively. Unlike conventional methods such as OSC, S/B, DS, and PDS, this novel approach does not require the analysis of identical samples across different instruments. This characteristic significantly broadens its applicability for model transfer, which is particularly beneficial for transferring specific measurement samples.

Evaluation of phthalate migration potential in vacuum-packed

In recent years, the presence and migration of PAEs in packaging materials and consumer products has become a serious concern. Based on this concern, the aim of our study is to determine the possible migration potential and speed of PAEs in benthic fish stored in vacuum packaging, as well as to monitor the storage time and type as well as polyethylene (PE) polymer detection.As a result of the analysis performed by µ-Raman spectroscopy, 1 microplastic (MP) of 6 µm in size was determined on the 30th day of storage in whiting fish muscle and the polymer type was found to be Polyethylene (PE) (low density polyethylene: LDPE). Depending on the storage time of the packaging used in the vacuum packaging process, it has been determined that its chemical composition is affected by temperature and different types of polymers are formed. 10 types of PAEs were identified in the packaging material and stored flesh fish: DIBP, DBP, DPENP, DHEXP, BBP, DEHP, DCHP, DNOP, DINP and DDP. While the most dominant PAEs in the packaging material were determined as DEHP, the most dominant PAEs in fish meat were recorded as BBP and the lowest as DMP. The findings provide a motivating model for monitoring the presence and migration of PAEs in foods, while filling an important gap in maintaining a safe food chain.

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

The identification of different kinds of watercolor inks is an important work in the field of forensic science. Four different kinds of watercolor ink Spectroscopy data fusion strategies (Fourier Transform Infrared spectroscopy and Raman spectroscopy) combined with a non-linear classification model (Extreme Learning Machine) were used to identify the brand of watercolor inks. The study chose Competitive Adaptive Reweighted Sampling (CARS), Random Frog (RF), Variable Combination Population Analysis-Genetic Algorithm (VCPA-GA), and Variable Combination Population Analysis-Iteratively Retains Informative Variables (VCPA-IRIV) to extract characteristic variables for mid-level data fusion. The Cuckoo Search (CS) algorithm is used to optimize the extreme learning machine classification model. The results showed that the classification capacity of the mid-level fusion spectra model was more satisfactory than that of single Infrared spectroscopy or Raman spectroscopy. The CS-ELM models based on infrared spectroscopy used to recognize the watercolor ink according to brands (ZHENCAI, DELI, CHENGUANG, and STAEDTLER) obtained an accuracy of 66.67% in the test set using all spectral datasets. The accuracy of CS-ELM models based on Raman spectroscopy was 67.39%. The characteristic wavelength selection algorithms effectively improved the accuracy of the CS-ELM models. The classification accuracy of the mid-level spectroscopy fusion model combined with the VCPA-IRIV algorithm was 100%. The data fusion method increased effectively spectral information. The method could satisfactorily identify different brands of watercolor inks and support the preservation of artifacts, paintings, and forensic document examination.