Rapid Quality Assessment Research Articles

Evaluation of rapid evaporative ionization mass spectrometry (REIMS) for the prediction of slice shear force and quality grades in beef longissimus lumborum steaks.

Steak samples were collected from the longissimus lumborum muscles of beef carcasses (Canada AA, n=1505; Canada AAA, n=1363) over a 3-year period. Steaks were aged for 14 d, and tenderness was determined by slice shear force (SSF). Metabolomic profiling of beef samples was performed using rapid evaporative ionization mass spectrometry (REIMS) (N=2853). Thirteen machine learning algorithms were used to build predictive models. Data were reduced using two separate approaches, one being feature selection (FS) and the second principal component analysis followed by FS (PCA-FS). No models could predict SSF tenderness category using FS and PCA-FS datasets with higher accuracy than the no information rate (NIR; 59.5%, P≥0.05). Population mean and standard deviation (SD) were calculated to generate 4 SD categories (±2) for further predictions. No model could predict SD category using the FS dataset (NIR=55.1%, P>0.05). Top accuracies for PCA-FS were generated from the Treebag and Random Forest (RF) algorithms (82.8% and 83.0%, respectively; NIR=55.0%, P<0.001). Top accuracies for FS were generated from SVM Radial and XGBoost to predict quality grade (84.6% and 85.3%, respectively NIR=52.5%, P<0.001). Top accuracies for PCA-FS were generated from SVM Radial and RF (82.8% and 84.2%, respectively, P<0.001). A stepwise regression model was built to evaluate relationships between SSF values and spectra generated from REIMS. Selected REIMS bins accounted for 7.2% of the variation in predicted SSF values (R2=0.072; P<0.001). With more development, the RF algorithm could assist REIMS in rapid assessment of carcass quality.

Poor sleep quality is a risk factor for adverse clinical outcomes in patients with acute aortic dissection: A prospective cohort study.

The decrease in sleep quality leads to an increased risk of cardiovascular disease and is closely related to the prognosis of various diseases. However, the relationship between preoperative sleep quality and postoperative clinical outcomes in patients with acute aortic dissection is still unclear. We aimed to assess the relationship between sleep quality and the clinical outcomes of acute aortic dissection. We assessed participants' sleep quality and the clinical outcomes of acute aortic dissection, which included prolonged mechanical ventilation, postoperative delirium, in-hospital death, continuous renal replacement therapy, intensive care unit stay time, and length of stay. Patients were divided into good sleep quality group (n = 103) and poor sleep quality group (n = 113). Postoperative delirium, in-hospital death, prolonged mechanical ventilation and intensive care unit stay time were significantly increased in the poor sleep quality group when compared with the good sleep quality group (p < 0.05). Multivariate regression analysis showed that the poorer sleep quality, the greater the risk of in-hospital death (odds ratio = 3.451, 95% confidence interval 1.19-10.004) and prolonged mechanical ventilation (odds ratio = 6.302, 95% confidence interval 3.105-12.791), and the longer intensive care unit stay time (β = 62.37, 95% confidence interval 22.411-102.329). In addition, the higher the incidence of smoking history (odds ratio = 7.417, 95% confidence interval 2.425-22.684), poor sleep quality (odds ratio = 11.59, 95% confidence interval 3.844-34.942) and postoperative delirium (odds ratio = 5.1, 95% confidence interval 1.793-14.504), the greater the risk of prolonged mechanical ventilation. Our findings revealed that poor sleep quality may be a risk factor for adverse clinical outcomes of acute aortic dissection. Rapid assessment of self-reported sleep quality may be a simple and effective way to identify patients with acute aortic dissection who are at high risk for prolonged mechanical ventilation.

Підходи до розроблення інформаційної технології швидкої ідентифікації екологічного стану об’єктів навколишнього середовища

Introduction. The task of express measurement of water pollution is a pressing issue of the 20th century. Development of methods and, based on them, technologies and devices that are easy to use and affordable will allow determining the concentration of toxic substances in water in order to assess its quality. The use of fast systems for determining the ecological state of water bodies will help reduce the risk of consuming contaminated water. The purpose of the research is to analyze approaches to express measurement of the concentration of toxic substances in water to develop approaches to building an information technology for rapid assessment of water quality. Results. An analysis of approaches to express measurement of the concentration of toxic substances in water was conducted. A review of methods for determining the concentration of toxic substances in water was conducted. The development of electrochemical methods of analysis and the use of various sensors for determining the concentrations of individual elements were studied, approaches to the development of practical automated systems for electrochemical studies and the features of the use of information technologies for processing analytical signals were considered. Conclusions. The conducted analysis showed that it is advisable to measure the concentrations of toxic elements lead, cadmium, copper, zinc, phosphorus in water using pulse methods of inversion chronopotentiometry and sensors made of noble metals. Keywords: information technology, hardware and software complex, methods of analysis, water pollution, express measurement, heavy metals, concentration of toxic substances/ assessment of quality, modeling, identification, monitoring, automated system.

Modelling and nondestructive quality prediction of Guanxi Honey Red Pomelo (&lt;i&gt;Citrus grandis&lt;/i&gt; (L.) Osbeck) based on shape characteristics and bitterness

Currently, it is difficult to distinguish the inner quality of pachycarpous fruits using nondestructive instruments, and trace nutrients and flavor compounds are rarely considered in fruit sorting. In this study, the external features and internal quality indices of an extensively farmed citrus fruit, the Guanxi honey red pomelo, were measured. The correlation between the inner and external quality was analyzed using Pearson correlation, canonical correlation, and grey correlation analyses. This research innovatively introduced bitter substances and revealed that the height-diameter ratio could serve as a predictor for ultra-size fruits, which partially reflects inner quality indices. Several grey formulas were developed to express these correlations and were fitted to a matrix, which demonstrated accuracies at all level 1 (C &lt; 0.35, P &gt; 0.95). Based on the matrix, fruit quality analysis of white and red pomelo showed high prediction accuracy in TSS and bitter substance indices (R2 = 0.6 – 0.8) and indicated that mature and symmetrical pomelo (height-diameter ratio of 0.9 – 1.1) exhibit a similar quality pattern. This study achieved rapid quality assessment using easy measurable shape characteristics and explored the application of characteristic bitter substances in the quality evaluation model, providing theoretical references and potential application guidance for pachycarpous fruit sorting, as well as introducing a new idea to incorporating characteristic quality into modeling index.

The Accurate and Exclusive Quantification of Somatic Cells in Raw Milk with an OPD-Cu2+ System-Based Colorimetric Method.

The somatic cell count (SCC) refers to the number of somatic cells present in each milliliter of raw milk and serves as a crucial indicator of dairy cow udder health and raw milk quality. Traditional SCC detection methods are often time-consuming, expensive, and susceptible to bacterial interference, rendering them unsuitable for the rapid and unbiased assessment of raw milk quality. Consequently, there is an urgent need for a low-cost, accurate, and user-friendly SCC quantification method. Here, a method based on an OPD-Cu2+ system for SCC quantification was developed. It was found that OPD oxidation signals exhibited a linear correlation with SCC. Following optimization, the detection system was established with a Cu2+ concentration of 25 μM, an OPD concentration of 2 mM, and an incubation time of 15 min. Furthermore, the method demonstrated significant resistance to bacterial interference, though it produced weaker signals in response to bacteria. The somatic cell recovery rate in milk after pretreatment was 88.9%, and SCC was quantified accurately within 45 min, with a linear range of 104-106 cells/mL. In summary, the method developed is cost-effective, straightforward, and facilitates precise somatic cell quantification, offering significant practical value and a new approach for SCC detection in raw milk.

Volatile compounds and quality changes of lesser mealworm larvae: effect of blanching, storage temperature and time

Abstract The impact of blanching and storage temperature (−20 °C and −80 °C) on the formation of volatile organic compounds and changes in quality characteristics of lesser mealworm (Alphitobius diaperinus) larvae was examined over seven weeks. Acid and peroxide values, pH, and colour of lesser mealworm larvae were significantly affected by the studied variables. The changes in these characteristics as well as in the volatile profile indicated occurring lipid oxidation and lipid hydrolysis processes during the storage. The intensity of these reactions was mostly influenced by blanching treatment and storage time, and a lesser extent by storage temperature. It was proposed that changes in composition and intensity of volatiles can be used for rapid quality assessment of lesser mealworm larvae during frozen storage. Thus, the selection of optimum storage conditions for lesser mealworm larvae is important for the prevention of undesirable chemical reactions and preservation of the quality of insect biomass.

An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy

Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, β-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency.Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%.Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, β-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370.This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.

Few shot intelligent assessment method for compaction quality of earthwork considering uncertainty

Current practices lack reliable models for continuously assessing compaction quality from limited data, which hinders effective compaction quality control and the acceptance of earthwork. Using material source parameters as inputs for assessment models makes it infeasible to achieve a real-time assessment of the compaction quality in earthwork. These issues have hindered the popularization of emerging intelligent compaction (IC) and intelligent rolling compaction (IRC) technologies. Therefore, this study addresses the need for small-sample training by introducing an uncertainty-based multi-population genetic algorithm fused with a backpropagation neural network model (UB-MPGA-BP), enabling the real-time intelligent assessment of compaction quality based on few-shot learning. In case studies involving high- and low-liquid limit silts, existing models trained on large datasets were notably inadequate for small-sample evaluation tasks. However, the proposed method not only performed well in silt cases but also exhibited high performance in rockfill materials. The analysis revealed that in small-sample scenarios without reliance on material source parameters, the proposed method has the advantages of real-time accuracy, generalization, few-shot data-driven, and simple input parameters, which enable the rapid assessment of compaction quality. The model can potentially facilitate IC/IRC applications in other engineering practices.

Fisheye Night Sky Imager: A Calibrated Tool to Measure Night Sky Brightness

The Night Skies Team of the U.S. National Park Service (NPS) has developed a new camera system equipped with a fisheye lens to measure night sky brightness. This NPS Fisheye Night Sky Imager comprises a back-illuminated Sony IMX455 CMOS sensor housed in the ZWO ASI6200MM camera, a Johnson V filter, and a Sigma 8 mm F3.5 fisheye lens. All of the components are commercially available. The fisheye lens allows us to capture the entire sky in a single frame. The wide-angle view also introduces calibration challenges. To address this, we have customized tools and devised innovative methods for calibrating the system, including flat-field correction, pixel scale determination, and field of view characterization. Additionally, we use standard stars for precise positional and photometric calibration. Furthermore, we create an open-source Python pipeline to process these fisheye images and leverage Git and GitHub for source code version control and distribution. The resulting images provide a positionally and brightness-calibrated fisheye view of the night sky, with a photometric calibration uncertainty of 0.12 mag. When comparing our Fisheye Night Sky Imager measurements to images taken with other calibrated systems, we reliably achieve consistent results under both dark and bright skies. Our Fisheye Night Sky Imager not only enables rapid assessment of sky quality but also supports the feasibility of establishing permanent monitoring locations. Recognizing that the natural dark sky is a critical element for natural, cultural, educational, and visitor experiences, this new camera system enhances our ability to assess resource conditions and fulfill the NPS mission of conserving resources unimpaired for the enjoyment of this and future generations.

Low-cost, immediate, general-purpose, and high-throughput (LIGHt) smartphone colorimetric screening assay for water-soluble protein

An efficient and rapid method for the detection of total soluble protein in tobacco leaves, utilizing a smartphone-based colorimetric approach has been developed. The proposed low-cost, immediate, general-purpose, and high-throughput (LIGHt) smartphone colorimetric screening assay integrates commercially available microplates, enabling on-site, high-throughput screening of tobacco leaf quality. The study involves preparing protein standard solutions and constructing standard curves using both spectrophotometric and smartphone-based methods. The LIGHt smartphone colorimetry yielded an average relative standard deviation of 10.6 %, a limit of detection of 2 μg/mL, and an average recovery of 93 %. The results demonstrated a comparable performance between intensities from the blue channel and the absorbance values in reflecting protein concentrations, validating the feasibility of utilizing smartphone colorimetry for protein concentration determination. Our approach demonstrates the potential for practical implementation in the field, providing a cost-effective and user-friendly solution for rapid quality assessment in the tobacco industry. The LIGHt smartphone colorimetry enhances quality control practices in the tobacco sector and offers a promising tool for on-site production quality testing in various industries, such as fruits and vegetables.

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Membrane-bound chemoreception of bitter bile acids and peptides is mediated by the same subset of bitter taste receptors

The vertebrate sense of taste allows rapid assessment of the nutritional quality and potential presence of harmful substances prior to ingestion. Among the five basic taste qualities, salty, sour, sweet, umami, and bitter, bitterness is associated with the presence of putative toxic substances and elicits rejection behaviors in a wide range of animals including humans. However, not all bitter substances are harmful, some are thought to be health-beneficial and nutritious. Among those compound classes that elicit a bitter taste although being non-toxic and partly even essential for humans are bitter peptides and l-amino acids. Using functional heterologous expression assays, we observed that the 5 dominant human bitter taste receptors responsive to bitter peptides and amino acids are activated by bile acids, which are notorious for their extreme bitterness. We further demonstrate that the cross-reactivity of bitter taste receptors for these two different compound classes is evolutionary conserved and can be traced back to the amphibian lineage. Moreover, we show that the cross-detection by some receptors relies on “structural mimicry” between the very bitter peptide l-Trp-Trp-Trp and bile acids, whereas other receptors exhibit a phylogenetic conservation of this trait. As some bile acid-sensitive bitter taste receptor genes fulfill dual-roles in gustatory and non-gustatory systems, we suggest that the phylogenetic conservation of the rather surprising cross-detection of the two substance classes could rely on a gene-sharing-like mechanism in which the non-gustatory function accounts for the bitter taste response to amino acids and peptides.

Enhancing quality assessment of live Pacific oysters (Crassostrea gigas) using flexible sensors real‐time monitoring physiological signals

AbstractShell‐closing strength (SCS) is commonly used to assess oyster vigor and quality as an important physiological stress indicator of the oyster organism. In this study, a quality decision support system based on flexible wireless sensor network and web services was designed and developed to explore the application of SCS in the rapid assessment of vitality and quality. Based on physiological and environmental information collected by flexible wireless sensor networks, this study proposes a novel quantitative vitality assessment method to identify oyster life decline cycles and processes. Meanwhile, TVB‐N, TVC, and pH were selected as quality indicators, and a back‐propagation artificial neural network (BP‐ANN) was used to predict and evaluate oyster quality indicators. The results showed that the root mean square errors (RMSE) for TVB‐N, TVC, and pH were 0.4624, 0.8827, and 0.1941; the coefficients of determination (R2) were 0.8919, 0.8578, and 0.6249. Therefore, the rapid assessment of oyster vitality and quality based on a flexible wireless sensor network is a reliable and effective means.Practical applicationsThis study aims to explore the application of live oyster SCS for rapid quality evaluation. In this article, a quality decision support system based on a flexible wireless sensor network (WSN) and web services is developed to improve the quality and health of live oysters in the supply chain. The use of the multi‐sensor system enables the monitoring and collection of environmental and physiological signals of live oysters throughout the supply chain process. The evaluation of the quality of live oysters using physiological and environmental information collected by flexible wireless sensor networks is a reliable and efficient method. This will provide an effective and reliable quality evaluation and management for the oyster industry.

Urban Ecological Quality Assessment Based on Google Earth Engine and Driving Factors Analysis: A Case Study of Wuhan City, China

Ecological quality is a critical factor affecting the livability of urban areas. Remote sensing technology enables the rapid assessment of ecological quality (EQ), providing scientific theoretical support for the maintenance and management of urban ecology. This paper evaluates and analyzes the EQ and its driving factors in the city of Wuhan using remote sensing data from five periods: 2001, 2006, 2011, 2016, and 2021, supported by the Google Earth Engine (GEE) platform. By employing principal component analysis, a Remote Sensing Ecological Index (RSEI) was constructed to assess the spatiotemporal differences of EQ in Wuhan City. Furthermore, the study utilized the optimal parameter-based geographical detector model to analyze the influence of factors such as elevation, slope, aspect, population density, greenness, wetness, dryness, and heat on the RSEI value in 2021 and further explored the impact of changes in precipitation and temperature on the EQ in Wuhan. The results indicate that (1) principal component analysis shows that greenness and wetness positively affect Wuhan’s EQ, while dryness and heat have negative impacts; (2) spatiotemporal analysis reveals that from 2001 to 2021, the EQ in Wuhan showed a trend of initial decline followed by improvement, with the classification grades evolving from poor and average to good and better; (3) the analysis of driving factors shows that all nine indicators have a certain impact on the EQ in Wuhan, with the influence ranking as NDVI &gt; NDBSI &gt; LST &gt; WET &gt; elevation &gt; population density &gt; GDP &gt; slope &gt; aspect; (4) the annual average temperature and precipitation in Wuhan have a non-significant impact on the EQ. The EQ in Wuhan has improved in recent years, but comprehensive management still requires enhancement.

Seabream Quality Monitoring Throughout the Supply Chain Using a Portable Multispectral Imaging Device

Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision−making, reducing food waste, and increasing sustainability. A portable multispectral imaging sensor was used for the rapid prediction of microbiological quality of fish fillets. Seabream fillets, packaged either in aerobic or vacuum conditions, were collected from both aquaculture and retail stores, while images were also acquired both from the skin and the flesh side of the fish fillets. In parallel to image acquisition, the microbial quality was also estimated for each fish fillet. The data were used for the training of predictive artificial neural network (ANN) models for the estimation of total aerobic counts (TACs). Models were built separately for fish parts (i.e., skin, flesh) and packaging conditions and were validated using two approaches (i.e., validation with data partitioning and external validation using samples from retail stores). The performance of the ANN models for the validation set with data partitioning was similar for the data collected from the flesh (RMSE = 0.402–0.547) and the skin side (RMSE = 0.500–0.533) of the fish fillets. Similar performance also was obtained from validation of the models of the different packaging conditions (i.e., aerobic, vacuum). The prediction capability of the models combining both air and vacuum packaged samples (RMSE = 0.531) was slightly lower compared to the models trained and validated per packaging condition, individually (RMSE = 0.510, 0.516 in air and vacuum, respectively). The models tested with unknown samples (i.e., fish fillets from retail stores-external validation) showed poorer performance (RMSE = 1.061–1.414) compared to the models validated with data partitioning (RMSE = 0.402–0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated. Hence, these outcomes could be beneficial not only for the industry and food operators but also for the authorities and consumers.

Rapid Quality Assessment of Polygoni Multiflori Radix Based on Near-Infrared Spectroscopy

The precise and prompt determination of quality control indicators such as moisture, stilbene glycosides, and anthraquinone glycosides is crucial in assessing the quality of Polygoni Multiflori Radix. Near-infrared spectroscopy is a nondestructive analytical technique that offers a more desirable approach than traditional methods for assessing content levels. In this study, various spectral preprocessing techniques were used to preprocess the raw spectral data. The spectral data were correlated with the determination of three-component contents using the partial least squares regression (PLSR) method. Then different algorithms, such as competitive adaptive weighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE), and random frog hopping (RF), were used for model simplification and feature selection. The data suggest that the first-order deconvolution derivative (1st Dev.) processing of the spectral data is superior to other methods in all three model evaluation metrics. The PLSR model for moisture, stilbene glycosides, and anthraquinone glycosides produced the calibration coefficient of determination (R2C) of 0.82, 0.52, and 0.58, the root mean square error of cross validation (RMSECV) of 0.91%, 0.77%, and 0.69%, the prediction coefficient of determination (R2P) of 0.72, 0.28, and 0.54, the root mean square error of prediction (RMSEP) of 0.65%, 0.81%, and 0.75%, and relative percentage differences (RPDs) of 1.7, 1.0, and 0.8. After optimizing the model using CARS, R2C increased by 0.15%, 0.41%, and 0.34%, RMSECV decreased by 0.53%, 0.32%, and 0.24%, R2P increased by 0.21%, 0.63%, and 0.35%, RMSEP decreased by 0.36%, 0.41%, and 0.31%, and RPD increased by 1.1, 0.9, and 0.6, significantly improving the predictive capacity of the model. This research provides a feasible method for rapid compliance testing of Polygoni Multiflori Radix. To further improve the model’s performance and applicability, it is necessary to continuously expand the sample set with different varieties and locations for wide variation.

Rapid Determination of Diverse Ganoderic Acids in Ganoderma Using UPLC–MS/MS

Background:: Ganoderma is known for its pharmaceutical, nutritional, and functional benefits. Its primary bioactive components are ganoderic acids. However, previous quantification methods only analyzed an individual or limited number of ganoderic acids. This study aims to develop a reliable method for simultaneously quantifying the major ganoderic acids to enhance Ganoderma quality control and study its active ingredients. Methods:: We developed a rapid quality assessment method to simultaneously determine the eleven ganoderic acids in Ganoderma using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The sample extraction method, along with mass spectrometric detection and chromatographic separation conditions was optimized. The separation was carried out using the ACQUITY UPLC BEH C18 column with a gradient elution of 0.1% (v/v) formic acid in water and acetonitrile. The mass spectrometry utilized negative mode electrospray ionization (ESI), with quantitative analysis being carried out in the MRM mode. Results:: The calibration curves showed good correlation coefficients (r2 &gt; 0.998). The recovery range was 89.1–114.0%. The intra-day and inter-day relative standard deviation (RSD) were below 6.8% (n = 6) and 8.1% (n = 6), respectively. Furthermore, the detection and quantification limits were 0.66–6.55 μg/kg and 2.20–21.84 μg/kg, respectively. All 11 ganoderic acids in the sample solution remained stable at room temperature for 72 hours. A total of 11 ganoderic acids were quantified in the 13 Ganoderma samples. The levels of ganoderic acids were higher in Ganoderma lucidum than in Ganoderma sinense. Conclusion:: The method developed in this study can quantify ganoderic acids in Ganoderma lucidum, thus establishing a technical foundation for evaluating the Ganoderma quality.

The ability of a handheld near-infrared spectrometer to do a rapid quality assessment of bovine colostrum, including the immunoglobulin G concentration

Portable infrared-based instruments have made significant contributions in different research fields. Within the dairy supply chain, for example, most of portable devices are based on near-infrared spectroscopy (NIRS) and are nowadays an important support for farmers and operators of the dairy sector, allowing fast decision-making, particularly for feed and milk quality evaluation and animal health and welfare monitoring. The affordability, portability, and ease of use of these innovative devices have been pivotal factors for the implementation in dairy farms. In fact, pocket-sized devices enable non-expert users to perform quick, low cost and non-destructive analysis on various samples without complex preparation. As bovine colostrum (BC) quality is mostly given by the Ig G (IgG) level, evaluating the ability of portable NIRS tools to measure antibodies concentration is advisable. In this study we used the wireless device SCiO manufactured by Consumer Physics Inc. (Tel Aviv, Israel) to collect BC spectra and then attempt to predict IgG concentration and gross and fine composition in individual samples collected as soon as possible after calving (<6 h) in primiparous and pluriparous Holstein cows farmed in 9 Italian farms. Chemometric analyses revealed that SCiO has promising predictive performance for colostral IgG concentration, total Ig concentration, fat, and AA (R2CV ≥ 0.75). Excellent accuracy was observed for dry matter, protein, and S prediction in cross-validation and good prediction ability in external validation (R2CV ≥ 0.93; R2V ≥ 0.82). Nonetheless, SCiO's ability to discriminate between good- and low-quality samples was satisfactory. The affordable cost, the accurate predictions, and the user-friendly design coupled with the increased interest in colostrum quality within the dairy sector may boost the collection of extensive BC data for management and genetic purposes in the near future.

Machine learning combined with multi-source data fusion for rapid quality assessment of yellow rice wine with different aging years

Aging is a crucial factor for high-quality alcoholic beverages, which improves the flavor of alcoholic beverages such as yellow rice wine (YRW). However, traditional analytical methods for evaluating the quality of YRW are very tedious, and there is still a lack of rapid quality assessment methods for YRW with different aging times. In this study, taking Jimo rice wine (JRW, a representative YRW in northern China) with different aging years as the object, traditional analytical methods and various antioxidant experiments were used to characterize their quality differences, a rapid quality evaluation method was established by machine learning combining with the data obtained from Flash GC e-nose and near-infrared. The results showed multiple physicochemical parameters coupled with multivariate statistical analysis could distinguish JRW with diverse aging years. JRW with different aging years also showed various antioxidant capacities and generally increased with the aged years. A total of 24 major aroma components were identified in JRW, of which five varied regularly with aging time. Then, the deep learning algorithm (long short-term memory, LSTM) showed excellent classification performance (100% accuracy) in JRW with different aging years, and a multi-source information fusion strategy can achieve 100% classification accuracy even when combined with traditional algorithms. Finally, the fused data improved the accuracy of the LSTM regression model in predicting the content of the main physicochemical parameters of JRW, with higher R2 and lower RMSE compared to data from a single source. Overall, this study clarified the quality differences of JRW with diverse aging years, and a rapid and precise method combining multi-source data fusion and machine learning was developed to assess the quality of JRW, which could also apply to other beverages or foods.

Features in visible and Fourier transform infrared spectra confronting aspects of meat quality and fraud

Rapid assessment of microbiological quality (i.e., Total Aerobic Counts, TAC) and authentication (i.e., fresh vs frozen/thawed) of meat was investigated using spectroscopic-based methods. Data were collected throughout storage experiments from different conditions. In total 526 spectra (Fourier transform infrared, FTIR) and 534 multispectral images (MSI) were acquired. Partial Least Squares (PLS) was applied to select/transform the variables. In the case of FTIR data 30 % of the initial features were used, while for MSI-based models all features were employed. Subsequently, Support Vector Machines (SVM) regression/classification models were developed and evaluated. The performance of the models was evaluated based on the external validation set. In both cases MSI-based models (Root Mean Square Error, RMSE: 0.48–1.08, Accuracy: 91–97 %) were slightly better compared to FTIR (RMSE: 0.83–1.31, Accuracy: 88–94 %). The most informative features of FTIR for the case of quality were mainly in 900–1700 cm−1, while for fraud the features were more dispersed.