A feasibility of nondestructive rapid detection of total volatile basic nitrogen content in frozen pork based on portable near-infrared spectroscopy

Characterizing Chinese saffron Origin, Age and grade using VNlR hyperspectral imaging and Machine learning

Saffron (Crocus sativus L.), the dried stigma, is an extremely valuable spice and medicinal herb, whose economic value is affected by geographical origin, age and grade. In this study, we proposed a method to identify saffron from different Chinese origins, ages and grades, which was based on visible-near infrared hyperspectral imaging (VNIR-HSI), machine learning and data fusion strategies. Firstly, saffron samples were graded according to lSO2011/2010 standards, with age having a greater influence on grade than geographical origin. By comparing the effectiveness of different classification algorithms with different preprocessing methods, the results showed that MSC-CARS-SVM was an effective spectral classification algorithm to determine saffron origin and FD-CARS-SVM was an effective spectral classification algorithm to determine saffron age and grade. Finally, image and spectral features were fused at a mid-level to establish classification models for origin, age and grade, and the results showed that origin and age models were more effective after fusion than the initial spectral information, with prediction accuracies of 98.3% and 97.9%. However, the spectral FD-CARS-SVM model was found to be the most discriminative with a prediction accuracy of 89.6% for grade identification. This study provides a theoretical basis and technical support to characterize saffron quality for industry and consumers.

Data fusion of near-infrared and Raman spectroscopy: An innovative tool for non-destructive prediction of the TVB-N content of salmon samples

Total volatile basic nitrogen (TVB-N) serves as a crucial indicator for evaluating the freshness of salmon. This study aimed to achieve accurate and non-destructive prediction of TVB-N content in salmon fillets stored in multiple temperature settings (-20, 0, -4, 20 °C, and dynamic temperature) using near-infrared (NIR) and Raman spectroscopy. A partial least square support vector machine (LSSVM) regression model was established through the integration of NIR and Raman spectral data using low-level data fusion (LLDF) and mid-level data fusion (MLDF) strategies. Notably, compared to a single spectrum analysis, the LLDF approach provided the most accurate prediction model, achieving an R2P of 0.910 and an RMSEP of 1.922 mg/100 g. Furthermore, MLDF models based on 2D-COS and VIP achieved R2P values of 0.885 and 0.906, respectively. These findings demonstrated the effectiveness of the proposed method for precise quantitative detection of salmon TVB-N, laying a technical foundation for the exploration of similar approaches in the study of other meat products. This approach has the potential to assess and monitor the freshness of seafood, ensuring consumer safety and enhancing product quality.

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques

Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis-NIR, NIR, and the fused Vis-NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)-AdaBoost and XGBoost models were built based on the low-level fusion (LLF) and intermediate-level fusion (ILF) of single Vis-NIR and NIR as well as the fused data. The results showed that the SVM-AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with anR P 2 $\ R_P^2$ of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with anR P 2 $R_P^2$ of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains.

Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion

Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC), and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA), and variable combination population analysis and iterative retained information variable (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC + VCPA-IRIV + SVR (R2C = 0.995, R2P = 0.986, RPD = 8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.

Portable NIR spectroscopy: the route to green analytical chemistry

There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

Rapid detection of micronutrient components in infant formula milk powder using near-infrared spectroscopy

In order to achieve rapid detection of galactooligosaccharides (GOS), fructooligosaccharides (FOS), calcium (Ca), and vitamin C (Vc), four micronutrient components in infant formula milk powder, this study employed four methods, namely Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Normalization (Nor), and Savitzky-Golay Smoothing (SG), to preprocess the acquired original spectra of the milk powder. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm and Random Frog (RF) algorithm were used to extract representative characteristic wavelengths. Furthermore, Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) models were established to predict the contents of GOS, FOS, Ca, and Vc in infant formula milk powder. The results indicated that after SNV preprocessing, the original spectra of GOS and FOS could effectively extract feature wavelengths using the CARS algorithm, leading to favorable predictive results through the CARS-SVR model. Similarly, after MSC preprocessing, the original spectra of Ca and Vc could efficiently extract feature wavelengths using the CARS algorithm, resulting in optimal predictive outcomes via the CARS-SVR model. This study provides insights for the realization of online nutritional component detection and optimization control in the production process of infant formula.

Application of portable Vis-NIR spectroscopy for rapid detection of myoglobin in frozen pork

Myoglobin content is considered as a crucial index to evaluate the quality of frozen pork. In this study, a portable visible and near-infrared (Vis-NIR) spectrometer combined with chemometrics was used to detect myoglobin content in frozen pork. Metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin were assessed spectrophotometrically. The raw Vis-NIR spectra of frozen pork samples were pre-processed using 1^st derivatives (FD). Afterward, Synergy Interval Partial Least Square (Si-PLS) coupled Competitive Adaptive Reweighted Sampling algorithm (Si-CARS-PLS) was applied to select characteristic variables. The Si-CARS-PLS models revealed the probability of estimating myoglobin content in frozen pork, with predictive correlation coefficients (R_p) for metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin as 0.9095, 0.9004, 0.8578, and 0.9133, respectively. The findings of this study showed that Vis-NIR spectroscopy coupled with Si-CARS-PLS is a promising method and offered a way forward for determining the myoglobin content in frozen pork.

Preliminary investigation into the prediction of indicators of beef spoilage using Raman and Fourier transform infrared spectroscopy

The objective of this study was to assess the potential to predict the microbial beef spoilage indicators by Raman and Fourier transform infrared (FT-IR) spectroscopies. Vacuum skin packaged (VSP) beef steaks were stored at 0 °C, 4 °C, 8 °C and under a dynamic temperature condition (0 °C ∼ 4 °C ∼ 8 °C, for 36 d). Total viable count (TVC) and total volatile basic nitrogen (TVB-N) were obtained during the storage period along with spectroscopic data. The Raman and FTIR spectra were baseline corrected, pre-processed using Savitzky-Golay smoothing and normalized. Subsequently partial least squares regression (PLSR) models of TVC and TVB-N were developed and evaluated. The root mean squared error (RMSE) ranged from 0.81 to1.59 (log CFU/g or mg/100 g) and the determination coefficient (R²) from 0.54 to 0.75. The performance of PLSR model based on data fusion (combination of Raman and FT-IR data) is better than that based on Raman spectra and similar to that of FT-IR. Overall, Raman spectroscopy, FT-IR spectroscopy, and a combination of both exhibited a potential for the prediction of the beef spoilage.

In-situ and real-time monitoring of two-stage enzymatic preparation of ACE inhibitory peptides from Cordyceps militaris medium residues by ultrasonic-assisted pretreatment

A protocol for the preparation of angiotensin-Ⅰ-converting enzyme (ACE) inhibitory peptides from Cordyceps militaris medium residues (CMMR) was established by two-stage enzymatic hydrolysis (amylase and protease). In a combination (mono-, dual-, and tri-frequency) of five different frequencies (20, 28, 35, 40 and 50 kHz), ultrasound-assisted pretreatment increased ACE inhibition rate in hydrolysate by 63.30 % under the mode of 20/28 kHz. Afterwards, near-infrared (NIR) spectrometer combined with spectral preprocessing methods and multivariate analysis like partial least square (PLS), synergy interval-PLS (Si-PLS), random frog-PLS (RF-PLS) and competitive adaptive reweighted sampling (CARS-PLS) was used to monitor the ACE inhibitory activity. The performance of models was evaluated by the correlation coefficient (R_p) and root mean square error (RMSEP). CARS-PLS models achieved optimal results for both amylase and protease hydrolysis with R_p = 0.9693, RMSEP = 0.01 for the former and R_p = 0.9454, RMSEP = 0.03 for the latter. NIR spectrometer combined with CARS-PLS models may be employed for in-situ and real-time monitoring of the preparation of ACE inhibitory peptides under ultrasonic-assisted pretreatment.

Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology

Moisture content is an important indicator that affects green tea processing. In this study, taking Chuyeqi tea as the research object, a quantitative prediction model of the changes in moisture content during the processing of green tea was constructed based on machine vision and near-infrared spectroscopy technology. First, collect the spectrum and image information in the process of spreading, fixation, first-drying, carding, and second-drying. The competitive adaptive reweighted sampling (CARS) method is then used to extract the characteristic wavelengths in the spectrum, and the image's 9 color features and 6 texture features are combined to establish linear PLSR and nonlinear SVR prediction models by fusing the data information from the two sensors. The results show that, when compared to single data, the PLSR and SVR models based on low-level data fusion do not effectively improve the model's prediction accuracy, but rather produce poor prediction results. In contrast, the PLSR and SVR models established by middle-level data fusion have improved the prediction accuracy of moisture content in green tea processing. Among them, the established SVR model has the best effect. The correlation coefficient of the calibration set (Rc) and the root mean square error of calibration (RMSEC) are 0.9804 and 0.0425, respectively, the correlation coefficient of the prediction set (Rp) and the root mean square error of prediction (RMSEP) are 0.9777 and 0.0490 respectively, and the relative percent deviation is 4.5002. The results show that the middle data fusion based on machine vision and near-infrared spectroscopy technology can effectively predict the moisture content in the processing of green tea, which has important guiding significance for overcoming the low prediction accuracy of a single sensor.

Identification of characteristic volatiles and metabolomic pathway during pork storage using HS-SPME-GC/MS coupled with multivariate analysis

Previous researches have been conducted evaluating the volatile compounds of pork. However, data regarding the changes in volatiles and metabolic pathways during pork storage were inadequately investigated. Herein, a headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS) coupled multivariate analysis was proposed for characterizing the profiles of volatile compounds and metabolic pathways during pork storage. A total of 37 metabolites, including aldehydes, ketones, alcohols etc. were successfully identified. Multivariate statistical analysis revealed a substantial variation in metabolite phenotype among samples over the pork storage period, with 12 characteristic metabolites and 5 potential characteristic metabolites screened as biomarkers. Moreover, three metabolomic pathways analysis and transformation between each other (thermal reactions, lipid metabolism and amino acid metabolism) reveals the underlying mechanisms of metabolites change of pork. Therefore, the present study may provide insight into future understanding of the variation in the pork metabolite profiles.

Total volatile basic nitrogen and trimethylamine in muscle foods: Potential formation pathways and effects on human health

The use of total volatile basic nitrogen (TVB-N) as a quality parameter for fish is rapidly growing to include other types of meat. Investigations of meat quality have recently focused on TVB-N as an index of freshness, but little is known on the biochemical pathways involved in its generation. Furthermore, TVB-N and methylated amines have been reported to exert deterimental health effects, but the relationship between these compounds and human health has not been critically reviewed. Here, literature on the formative pathways of TVB-N has been reviewed in depth. The association of methylated amines and human health has been critically evaluated. Interventions to mitigate the effects of TVB-N on human health are discussed. TVB-N levels in meat can be influenced by the diet of an animal, which calls for careful consideration when using TVB-N thresholds for regulatory purposes. Bacterial contamination and temperature abuse contribute to significant levels of post-mortem TVB-N increases. Therefore, controlling spoilage factors through a good level of hygiene during processing and preservation techniques may contribute to a substantial reduction of TVB-N. Trimethylamine (TMA) constitutes a significant part of TVB-N. TMA and trimethylamine oxide (TMA-N-O) have been related to the pathogenesis of noncommunicable diseases, including atherosclerosis, cancers, and diabetes. Proposed methods for mitigation of TMA and TMA-N-O accumulation are discussed, which include a reduction in their daily dietary intake, control of internal production pathways by targeting gut microbiota, and inhibition of flavin monooxygenase 3 enzymes. The levels of TMA and TMA-N-O have significant health effects, and this should, therefore, be considered when evaluating meat quality and acceptability. Agreed international values for TVB-N and TMA in meat products are required. The role of feed, gut microbiota, and translocation of methylated amines to muscles in farmed animals requires further investigation.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.