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Traditionally, tenderness has been assessed through shear force testing, which is inherently destructive, the accuracy is easily affected, and it results in considerable sample wastage. Although this technology has some drawbacks, it is still the most effective detection method currently available. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into applying five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of the quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations to guide the future direction of non-destructive, AI-enhanced meat tenderness detection. [ABSTRACT FROM AUTHOR]

Apples are rich in vitamins and dietary fiber and are one of the essential fruits in people's daily diet. China has always been a big apple consumer, and with the improvement of people's life quality, nutrition, and health requirements, the demand for high-quality apples has increased year by year. Apple mold heart disease is one of the main diseases affecting apple quality. However, this disease cannot be easily detected from the surface, so it is difficult to detect mold heart disease. Therefore, this paper focuses on the analysis of seven non-destructive detection technologies, including near infrared spectroscopy technology, hyperspectral technology, Raman spectroscopy technology, electronic nose technology, acoustic technology, electrical technology, and magnetic technology, summarizes their application status in the detection of apple mold heart disease, and then analyzes their advantages and disadvantages. Combined with the current rapid development of artificial intelligence (AI) technology, this paper proposes the future development trends of using non-destructive technologies to detect apple mold heart disease. It is expected to provide basic theory and application references for the intelligent detection of apple mold heart disease. [ABSTRACT FROM AUTHOR]

Accurately determining the soluble solid content (SSC), titratable acidity (TA), and firmness of multiple pear varieties is crucial for enhancing their market appeal. Traditional methods using spectral techniques require laborious and trial-and-error preprocessing, feature-wavelength selection, and model-establishment process. Additionally, multiple independent models need to be trained to predict different attributes. In this study, we developed a multi-task convolutional neural network (MCNN) model based on the whale optimization algorithm (WOA) to establish a global model for simultaneously determination of SSC, TA, and firmness of three pear varieties. Spectral data in the range of 300–900 nm were collected from individual pear samples using a self-designed transmission spectra-acquisition device. We established and compared an individual-variety prediction model and a global model using three pear varieties. The optimized MCNN model achieved optimal prediction accuracies for the SSC (R v 2 = 0.977, residual prediction deviation [RPD] = 5.414), TA (R v 2 =0.970, RPD = 5.057), and firmness (R v 2 =0.978, RPD = 6.326). Combing the global-modeling strategy with optimized MCNN architecture enhanced the model's robustness to variety variation and improved the accuracy in detecting pear-quality traits. This model provides a new, convenient, and automated method for end-to-end and simultaneous predictions of multiple quality indicators in fruit without any further processing of raw spectra. • An WOA-based multi-task CNN global model was built for pear quality evaluation. • The robustness of near infrared model for multi-variety pear prediction was enhanced. • Simultaneously detecting SSC, TA and firmness in three pear varieties was achieved. • Preprocessing and feature variable selection were not required for MCNN model. [ABSTRACT FROM AUTHOR]

The traditional candling methods are very hard to discriminate the infertile chicken eggs from the fertile ones before hatching on the poultry farm. The purpose of this study was to detect infertile chicken eggs before hatching by use of the nondestructive technique of visible near infrared transmission spectroscopy. The Naïve-Bayes method was adopted to link spectral data and qualitative observation values of chicken eggs to build a model. Five preprocessing methods were applied to spectral data to improve the robustness of the models. The internal test method of leave one cross validation and an external test method were employed to test the predictive ability of the established models. Naïve-Bayes with a second derivative by the Savitzky-Golay preprocessing method was selected as the optimal prediction model by comparing with four evaluation parameters of other models. The overall accuracy of the model using the second derivative—Savitzky-Golay preprocessing method was 91.67%, while the sensitivity and the specificity were 95% and 100%, respectively. The prediction ability of the optimal model was assessed by use of unknown new egg samples. The average overall prediction accuracy of the prediction set containing 111 eggs was 89.12%, while the sensitivity and specificity were 88.14 and 90.38%, respectively. Results showed that the prediction model had good predictability and stability. Therefore, visible near infrared transmission spectroscopy would be an appropriate technology to detect infertile chicken eggs before hatching. The infertile chicken eggs from the fertile ones before hatching were discriminated Visible near infrared spectroscopy first detected infertile chicken eggs before hatching. Naive bayes classifier with five different pre-processing methods were compared. The unknown eggs were predicted by the optimal Naive bayes prediction model. [ABSTRACT FROM AUTHOR]

In this work, we quantified goat milk powder adulteration by adding urea, melamine, and starch individually and simultaneously, with the utilization of near infrared (NIR) spectroscopy coupled with chemometrics. For single-adulterant samples, the successive projections algorithm (SPA) selected three, three, and four optimal wavelengths for urea, melamine, and starch, respectively. Models were built based on partial least squares regression (PLS) and the selected wavelengths, exhibiting good predictive ability with an R p 2 above 0.987 and an RMSEP below 0.403%. For multiple-adulterants samples, PLS2 and multivariate curve resolution alternating least squares (MCR-ALS) were adopted to build the models to quantify the three adulterants simultaneously. The PLS2 results showed adequate precision and results better than those of MCR-ALS. Except for urea, MCR-ALS models presented good predictive results for milk, melamine, and starch concentrations. MCR-ALS allowed detection of adulteration with new and unknown substitutes as well as the development of models without the need for the usage of a large data set. [ABSTRACT FROM AUTHOR]

The visible and near-infrared spectroscopy (Vis/NIRS) models for sheep meat quality evaluation using only one type of meat cut are not suitable for other types. In this study, a novel portable Vis/NIRS system was used to simultaneously detect physicochemical properties (pH, color L*, a*, b*, cooking loss, and shear force) for different types of sheep meat cut, including silverside, back strap, oyster, fillet, thick flank, and tenderloin cuts. The results show that the predictive abilities for all parameters could be effectively improved by spectral preprocessing. The coefficient of determination (Rp2) and residual predictive deviation (RPD) of the optimal prediction models for pH, L*, a*, b*, cooking loss, and shear force were 0.79 and 3.50, 0.78 and 2.28, 0.68 and 2.46, 0.75 and 2.62, 0.77 and 2.19, and 0.83 and 2.81, respectively. The findings demonstrate that Vis/NIR spectroscopy is a useful tool for predicting the physicochemical properties of different types of meat cut. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Study spectral calibration model transfer between two varieties of eggs. • Global updating, direct standardization and slope/bias correction were studied. • Search optimal master variety by comparing two varieties of eggs. • Mahalanobis distance combined with principal component analysis eliminated outliers. Abstract This research was performed to study calibration model transfer between White Leghorns eggs and Bantam eggs for prediction of egg freshness by visible near infrared (VIS-NIR) spectroscopy. Transmission spectra of the two varieties were acquired in the equatorial region of the eggs. And albumen pH as the freshness evaluating parameter was measured using traditional destructive methods. After outliers were eliminated by Mahalanobis distance combined with principal component analysis (PCA), partial least squares regression (PLSR) with different preprocessing methods was used to develop prediction models. Global updating, direct standardization (DS) and slope/bias correction (SBC) were evaluated to transfer calibration models from one variety to another. The Kennard-Stone (KS) algorithm was used to select standardization samples. White Leghorns eggs and Bantam eggs as the master variety in turn were compared to find superior master variety. Application of the slope/bias correction (SBC) algorithm obtained the best prediction results of albumen pH. And the better slope/bias correction (SBC) transfer performance with a r p of 0.908 and a RMSEP of 0.133 was found when Bantam eggs were as the superior master variety. [ABSTRACT FROM AUTHOR]

In the meat industry, it is essential to monitor the total volatile basic nitrogen (TVB-N) content due to its impact on human diets safety. In the present study, two-dimensional correlation spectroscopy (2D-COS) was combined with dual-band visible/near-infrared spectroscopy to identify the characteristic wavelengths of TVB-N in pork meat during storage. Reflectance spectra of pork longissimus dorsi muscles at 350–1100 nm and 1000–2500 nm were obtained during a 13-day storage period. The dual-band spectra were merged via parabolic fitting, and subjected to pre-treatments using absorbance conversion, first derivation (FD), and standard normal variate transformation (SNV). Then, the storage day was used as the external perturbation factor, and 2D-COS was performed on the dynamic spectra to identify TVB-N-related wavebands. Twenty-five characteristic wavelengths were selected, and a simplified partial least square regression (PLSR) model was established with a correlation coefficient (R p) of 0.9591, root mean square error on prediction (RMSEP) of 1.8341 mg/100 g, and relative percent deviation (RPD) of 3.2518. These results outperformed those of the model based on full wavebands data (R p = 0.9215, RMSEP = 2.8191 mg/100 g, RPD = 2.1114). This study accurately predicted TVB-N values in pork meat, and the integration of 2D-COS and dual-band spectra had great potential for real-time meat freshness evaluation. • 2D-COS was employed to identify feature variables for TVB-N prediction in pork. • Dual-band visible/near infrared spectroscopy was used to improve model performance. • 25 characteristic wavelengths were selected to build simplified PLSR model. • TVB-N content was well predicted with R p of 0.9591 and RPD of 3.2518. [ABSTRACT FROM AUTHOR]