Calibration transfer between NIR instruments using optimally predictive calibration subsets.

In this study, a new approach for the selection of informative standardization samples from the original calibration set for the transfer of a calibration model between NIR instruments is proposed and evaluated. First, a calibration model is developed, after variable selection by the Final Complexity Adapted Models (FCAM) method, using the significance of the PLS regression coefficients (FCAM-SIG) as selection criterion. Then, the resulting model is used for the selection of the best fitting subset of calibration samples with optimally predictive ability, called the optimally predictive calibration subset (OPCS). Next, the standardization samples are selected from the OPCS. The spectra on the slave instruments are transferred to corresponding spectra on the master instrument by the widely used Piecewise Direct Standardization (PDS) method. Thereafter, for the test set on the slave instrument, a 3D response surface plot is drawn for the root mean squared error of prediction (RMSEP) as a function of the number of OPCS samples and window sizes used for the PDS method. Finally, the smallest set of calibration samples, in combination with the optimal window size, providing the optimal RMSEP, is selected as standardization set. The proposed OPCS approach for the selection of standardization samples is tested on two real-life NIR data sets providing 13 X-y combinations to model. The results show that the obtained numbers of OPCS-based standardization samples are statistically significantly lower than those obtained with the widely used representative sample selection method of Kennard and Stone, while the predictive performances are similar.

Full range leadership style and its effect on effectiveness, employee satisfaction, and extra effort: an empirical study.

The purpose of this study is to determine if there is a positive relationship between full-range leadership and employees' effort, efficiency, and satisfaction. A questionnaire was administered to 577 executives from Colombian companies, and the data was analyzed using a partial least squares structural equation modeling (PLS-SEM) approach. The results show that both transformational and transactional leadership have a direct and significant impact on extra effort, effectiveness, and satisfaction, with transformational leadership having the greatest impact on these factors. Conversely, passive-avoidant leadership has negative effects on these three constructs. This study validates the effectiveness of the MLQ 5X in a South American country, a geographical region where such studies are in their early stages. Finally, the whole range of leadership styles-transformational, transactional, and passive-avoidant-is looked at. These styles are seen as second-order constructs that challenge latent multidimensional models as they emerge.

Geographic information systems adoption model: A partial least square-structural equation modeling analysis approach.

The ability of Geographic Information System (GIS) to organize, analyze, visualize and integrate spatial data has been at the top of its primary uses among professional industries. However, considering the extensive adoption of Information System (IS) throughout history for government organizations' or citizens' disaster response, the implementation of geographical elements is still minimal. Previous GIS models and framework studies, particularly in developing countries, were affected by pandemic pressure, competitiveness pressure, change management, and security factors. Thus, this study aims to develop a model for the successful adoption of GIS using the Technology Acceptance Model (TAM), and De Lone and Mc Lean Information Success Model and analyze the applicability of the existing factors to enhance the performance of Public Sector Organizations (PSOs). From the study, a new conceptual framework was proposed to examine the effects of factors on GIS adoption that impact performance among PSOs from the perspective of Saudi Arabia. Quantitative methods were used to collect data through a questionnaire distributed to 350 respondents from PSO, and only 272 were found to be valid. Partial Least Square Structural Equation Modeling (PLS-SEM) validated the GIS model. The finding revealed that system quality, service quality, change management, competitiveness pressure, perceived ease of use, perceived usefulness, and security factors significantly and positively affected GIS adoption. The study also showed that GIS adoption substantially affected PSO performance. The proposed model provides insight into how GIS adoption can eventually enhance performance among PSOs. In essence, the study contributes to the running of PSO and the decisions taken by policymakers.

NMR-based metabolomics for investigating urinary profiles of metal carpentry workers exposed to welding fumes and volatile organic compounds.

Introduction: Metal carpentry includes a wide range of work activities such as welding and cutting metallic components, use of solvents and paints. Therefore, the employees in these types of activities are mainly exposed to welding fumes and volatile organic solvents. Here, we present an NMR-based metabolomic approach for assessing urinary profiles of workers in the same company that are exposed to two different risk factors. Methods: The study enrolled 40 male subjects exposed to welding fumes, 13 male subjects exposed to volatile organic compounds of a metal carpentry company, and 24 healthy volunteers. All samples were collected, in the middle of the working week at fast. Thirty-five urinary metabolites belonging to different chemical classes such as amino acids, organic acids and amines were identified and quantified. Results were processed by multivariate statistical analysis for identifying significant metabolites for each working group examined, compared to controls. Results: Workers exposed to welding fumes displayed urinary increase in glutamine, tyrosine, taurine, creatine, methylguanidine and pseudouridine associated to oxidative impairment, while workers exposed to volatile organic compounds showed higher urinary levels of branched chain aminoacids. Conclusion: Our work identified specific urinary profile related to each occupational exposure, even if it is below the threshold limit values.

Development of partial least squares regression with discriminant analysis for software bug prediction.

Many prediction models and approaches have been introduced during the past decades that try to forecast bugged code elements based on static source code metrics, change and history metrics, or both. However, there is still no universal best solution to this problem, as most suitable features and models vary from dataset to dataset and depend on the context in which we use them. Therefore, novel approaches and further studies on this topic are highly necessary. In this paper, we employ a chemometric approach - Partial Least Squares with Discriminant Analysis (PLS-DA) - for predicting bug prone Classes in Java programs using static source code metrics. PLS-DA is successfully applied within the field of chemometrics, but to our best knowledge, it has never been used before in the software maintenance domain for predicting bugs. In addition, we have used rigorous statistical treatments and evaluation for representing the software engineering results. We show that our PLS-DA based prediction model achieves superior performances compared to the state-of-the-art approaches (i.e. F-measure of 0.44-0.47 at 90% confidence level) when no data re-sampling applied and comparable to others when applying up-sampling on the largest open bug dataset, while training the model is significantly faster, thus finding optimal parameters is much easier. In terms of completeness, which measures the amount of bugs contained in the Java Classes predicted to be defective, PLS-DA outperforms every other algorithm: it found 69.3% and 79.4% of the total bugs with no re-sampling and up-sampling, respectively.

The research on the impact of industry governance on trust after group product-harm crisis.

The group product-harm crisis has much greater and longer negative impact, and its governance has become an important issue. To address this issue, this study proposed a new construct: industry governance. On the basis of clarifying the dimensions and measurements of industry governance, this study constructed a reflective-formative hierarchical model and collected data through a questionnaire. Utilizing convenience sampling, 329 valid samples at the University in Wuhan, China collected by survey were used to verify the hypotheses. With the help of Smart PLS 3.0, this study finds that the industry governance has a significant and positive impact on consumer's trust (enterprise trust, industry trust and government trust) after the group product-harm crisis. Industry governance plays an important role in the governance of group product-harm crisis. This study is the first time to explore the structure and measurement of industry governance, and verifies the impact of industry governance on group product-harm crisis, which enriches governance theory, and perfects product-harm crisis theory, providing a new direction and guidance for managers to better manage product-harm crisis.

Near-infrared spectroscopy identification method of cashmere and wool fibers based on an optimized wavelength selection algorithm.

Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelength selection or feature extraction algorithms to obtain significant spectral features, but traditional algorithms often overlook the correlations between wavelengths, resulting in weak adaptability and local optimum issues. To address this problem, this paper proposes a recognition algorithm based on optimal wavelength selection, which can remove redundant information and make the model effective in capturing patterns and key features of the data. The wavelengths are rearranged by computing the information gain ratio for each wavelength. Then, the sorted wavelengths are grouped based on equal density, which ensures that all wavelengths within each group have equal information and avoids over-focusing on individual groups. Meanwhile, the group genetic algorithm is used to find the wavelengths with highly informative and search optimal grouped combinations, in order to explore the entire spectrum wavelength. Finally, combined with a partial least squares discriminant analysis(PLS-DA) model, the recognition accuracy reached 97.3 %. The results indicate that, compared to traditional methods such as CARS, SPA, and GA, our method effectively reduces redundant information, selects fewer but more informative wavelengths, and improves classification accuracy and model adaptability.

Variability in the home-field advantage of litter decomposition mediates alterations in soil CO2 and CH4 fluxes: A transplantation experiment study.

The decomposition of litter is susceptible to the influence of climate change and soil conditions, which can subsequently impact the release of carbon dioxide (CO2) from forest soils and the absorption of methane (CH4). Ecological theory proposes the existence of a home-field advantage (HFA) in litter decomposition, suggesting that the decomposition rate of litter (such as fallen leaves, twigs, and roots) may be faster in their native habitat than in foreign environments. Therefore, we selected litter from Pinus tabuliformis (PT) and Quercus acutissima Carruth (QC) in the field and conducted a 439-day litter transplant experiment to test the magnitude and direction of the HFA of these two litter types in three forest stands. During this experiment, we monitored the changes in soil CO2 and CH4 fluxes associated with the decomposition of PT and QC leaf litter in their native and foreign sites. Furthermore, we measured various soil physical, chemical, and biological indicators. The results indicated that the decomposition rate of QC leaf litter was faster in its native habitat, demonstrating a clear HFA effect. Conversely, the decomposition of PT leaf litter was observed to be more rapid in away soil, suggesting a pronounced home-field disadvantage (HFD). The study found that PT leaf litter exhibited greater CO2 release when decomposing in away soil, demonstrating 43 % and 32 % increases compared to bare soil, respectively. Conversely, QC leaf litter was observed to release more CO2 in its home soil. Additionally, the bare soils of the PT and QC home sites were found to absorb more CH4 compared to leaf litter coverage, with increases of 37.8 % and 31.2 %, respectively. The partial least squares model indicated that the litter attributes had a significant direct effect on soil temperature and enzyme activity. Soil temperature and enzyme activity further directly influenced the soil CO2 and CH4 fluxes. The results of our study indicate that the HFA of litter is dependent on litter type, and that litter transplantation can impact soil greenhouse gas exchange. This research provides a theoretical foundation for forest management and conservation strategies, as well as valuable data for global carbon neutrality efforts.

Understanding driving forces of food waste separation intention to enhance regional and local solid waste planning: application of PLS-SEM and multi-group analysis.

This study extended the theory of planned behaviour (TPB) and applied it to analyse influencing factors of food waste separation intention (FSI) among households in central Vietnam. Partial least squares structural equation modelling (PLS-SEM) was employed to analyse the data. The heterogeneity of factors contributing to FSI in cities of three levels was examined using multi-group analysis (MGA). The results indicate that attitudes, subjective norms (SN), perceived behavioural control (PBC), awareness of benefit (AB), information publicity (IP), facility availability (FA), and trust significantly affected FSI of households. MGA results indicated the heterogeneity of impacts of PBC and attitude on FSI of households among three municipal levels. The results will serve as basic data for waste officers, solid-waste management project leaders, non-governmental organisations (NGOs), and other related stakeholders to lay the foundation of food waste management planning in terms of regional scale and local scale. This study will also aid the creation of a circular economy by providing a scientific base for enhancing food waste separation at source in central Vietnam.

Characterization and Differentiation of Flavor Profile of 12 Air-Dried Yak Meat Products Using GC-IMS and Multivariate Analysis.

Volatile organic compounds (VOCs) in food are key factors constituting their unique flavor, while the characteristics of VOCs in air-dried yak meat (AYM) from various regions of the Tibetan Plateau and their inter-regional differences remain unclear. Therefore, this study conducted a comprehensive analysis of VOCs in the five-spice (FS), spicy and numbing (SN), and aromatic and spicy (AS) versions of AYM from four regions of the Tibetan Plateau (Gansu, Qinghai, Sichuan, and Tibet) using gas chromatography-ion mobility spectrometry (GC-IMS) A total of 58 VOCs were identified, with alcohols accounting for 28.40%, ketones 22.89%, aldehydes 18.85%, and terpenes 17.61%. Topographic plots, fingerprint profiles, and multivariate analysis not only distinguished AYM of the same flavor from different regions but also discriminated those of different flavors within the same region. Furthermore, 17 key VOCs were selected as the primary aroma characteristics of the 12 types of AYM, including linalool, 3-methylbutanal, acetone, and limonene. Meanwhile, the differential VOCs for each flavor were determined, with linalyl acetate being unique to the FS, (E)-ocimene and ethyl propanoate being specific to the SN, and 2-methyl-3-(methylthio)furan-D and Hexanal-D being characteristic of the AS flavor. Based on the above results, the flavor of AYM can be improved to suit the taste of most people and increase its consumption.

Mass spectrometry (MS)-based metabolomics of plasma and urine in dry eye disease (DED)-induced rat model.

Dry eye disease (DED) is an ophthalmic disease associated with poor quality and quantity of tears, and the number of patients is steadily increasing. The aim of this study was to determine plasma and urine metabolites obtained from DED scopolamine animal model where dry eye conditions (DRY) are induced. It was also of interest to examine whether DED (scopolamine) rat model was exacerbated by treatment with benzalkonium chloride (BAC). Subsequently, plasma and urine metabolites were analyzed using liquid chromatography (LC) and gas chromatography (GC)-mass spectrometry (MS), respectively. Data demonstrated that DED indicators such as tear volume, tear breakup time (TBUT), and corneal damage in the DED groups (DRY and BAC group) differed from those of control (CON). Similar results were noted in inflammatory factors such as interleukin (IL-1ß), IL-6, and tumor necrosis factor (TNF)-α. In the partial least squares-discriminant analysis (PLS-DA) score plots, the three groups were distinctly separated from each other. In addition, the related metabolites were also associated with these distinct separations as evidenced by 9 and 14 in plasma and urine, respectively. Almost all of the selected metabolites were decreased in the DRY group compared to CON, and the BAC group was lower than the DRY. In plasma and urine, lysophosphatidylcholine/lysophosphatidylethanolamine, organic acids, amino acids, and sugars varied between three groups, and these metabolites were related to inflammation and oxidative stress. Data suggest that treatment with scopolamine with/without BAC-induced DED and affected the level of systemic metabolites involved in inflammation and oxidative stress.

Analysis of the Pomelo Peel Essential Oils at Different Storage Durations Using a Visible and Near-Infrared Spectroscopic on Intact Fruit.

Pomelo fruit pulp mainly is consumed fresh and with very little processing, and its peels are discarded as biological waste, which can cause the environmental problems. The peels contain several bioactive chemical compounds, especially essential oils (EOs). The content of a specific EO is important for the extraction process in industry and in research units such as breeding research. The explanation of the biosynthesis pathway for EO generation and change was included. The chemical bond vibration affected the prediction of EO constituents was comprehensively explained by regression coefficient plots and x-loading plots. Visible and near-infrared spectroscopy (VIS/NIRS) is a prominent rapid technique used for fruit quality assessment. This research work was focused on evaluating the use of VIS/NIRS to predict the composition of EOs found in the peel of the pomelo fruit (Citrus maxima (J. Burm.) Merr. cv Kao Nam Pueng) following storage. The composition of the peel oil was analyzed by gas chromatography-mass spectrometry (GC-MS) at storage durations of 0, 15, 30, 45, 60, 75, 90, 105 and 120 days (at 10 °C and 70% relative humidity). The relationship between the NIR spectral data and the major EO components found in the peel, including nootkatone, geranial, ß-phellandrene and limonene, were established using the raw spectral data in conjunction with partial least squares (PLS) regression. Preprocessing of the raw spectra was performed using multiplicative scatter correction (MSC) or second derivative preprocessing. The PLS model of nootkatone with full MSC had the highest correlation coefficient between the predicted and reference values (r = 0.82), with a standard error of prediction (SEP) of 0.11% and bias of 0.01%, while the models of geranial, ß-phellandrene and limonene provided too low r values of 0.75, 0.75 and 0.67, respectively. The nootkatone model is only appropriate for use in screening and some other approximate calibrations, though this is the first report of the use of NIR spectroscopy on intact fruit measurement for its peel EO constituents during cold storage.

Variation Analysis of Starch Properties in Tartary Buckwheat and Construction of Near-Infrared Models for Rapid Non-Destructive Detection.

Due to the requirements for quality testing and breeding Tartary buckwheat (Fagopyrum tartaricum Gaerth), it is necessary to find a method for the rapid detection of starch content in Tartary buckwheat. To obtain samples with a continuously distributed chemical value, stable Tartary buckwheat recombinant inbred lines were used. After scanning the near-infrared spectra of whole grains, we employed conventional methods to analyze the contents of Tartary buckwheat. The results showed that the contents of total starch, amylose, amylopectin, and resistant starch were 532.1-741.5 mg/g, 176.8-280.2 mg/g, 318.8-497.0 mg/g, and 45.1-105.2 mg/g, respectively. The prediction model for the different starch contents in Tartary buckwheat was established using near-infrared spectroscopy (NIRS) in combination with chemometrics. The Kennard-Stone algorithm was used to split the training set and the test set. Six different methods were used to preprocess the spectra in the wavenumber range of 4000-12,000 cm-1. The Competitive Adaptive Reweighted Sampling algorithm was then used to extract the characteristic spectra, and the prediction model was built using the partial least squares method. Through a comprehensive analysis of each parameter of the model, the best model for the prediction of each nutrient was determined. The correlation coefficient of calibration (Rc) and the correlation coefficient of prediction (Rp) of the best models for total starch and amylose were greater than 0.95, and the Rc and Rp of the best models for amylopectin and resistant starch were also greater than 0.93. The results showed that the NIRS-based prediction model fulfilled the requirement for the rapid determination of Tartary buckwheat starch, thus providing an effective technical approach for the rapid and non-destructive testing of starch content in the food science and agricultural industry.

Discrimination and Quantification of Cotton and Polyester Textile Samples Using Near-Infrared and Mid-Infrared Spectroscopies.

In the textile industry, cotton and polyester (PES) are among the most used fibres to produce clothes. The correct identification and accurate composition estimate of fibres are mandatory, and environmentally friendly and precise techniques are welcome. In this context, the use of near-infrared (NIR) and mid-infrared (MIR) spectroscopies to distinguish between cotton and PES samples and further estimate the cotton content of blended samples were evaluated. Infrared spectra were acquired and modelled through diverse chemometric models: principal component analysis; partial least squares discriminant analysis; and partial least squares (PLS) regression. Both techniques (NIR and MIR) presented good potential for cotton and PES sample discrimination, although the results obtained with NIR spectroscopy were slightly better. Regarding cotton content estimates, the calibration errors of the PLS models were 3.3% and 6.5% for NIR and MIR spectroscopy, respectively. The PLS models were validated with two different sets of samples: prediction set 1, containing blended cotton + PES samples (like those used in the calibration step), and prediction set 2, containing cotton + PES + distinct fibre samples. Prediction set 2 was included to address one of the biggest known drawbacks of such chemometric models, which is the prediction of sample types that are not used in the calibration. Despite the poorer results obtained for prediction set 2, all the errors were lower than 8%, proving the suitability of the techniques for cotton content estimation. It should be stressed that the textile samples used in this work came from different geographic origins (cotton) and were of distinct presentations (raw, yarn, knitted/woven fabric), which strengthens our findings.

Metabolism changes caused by glucose in normal and cancer human brain cell lines by Raman imaging and chemometric methods.

Glucose is the main source of energy for the human brain. This paper presents a non-invasive technique to study metabolic changes caused by glucose in human brain cell lines. In this paper we present the spectroscopic characterization of human normal brain (NHA; astrocytes) and human cancer brain (CRL-1718; astrocytoma and U-87 MG; glioblastoma) control cell lines and cell lines upon supplementation with glucose. Based on Raman techniques we have identified biomarkers that can monitor metabolic changes in lipid droplets, mitochondria and nucleus caused by glucose. We have studied the vibrations at 750 cm-1, 1444 cm-1, 1584 cm-1 and 1656 cm-1 as a function of malignancy grade. We have compared the concentration of cytochrome, lipids and proteins in the grade of cancer aggressiveness in normal and cancer human brain cell lines. Chemometric analysis has shown that control normal, control cancer brain cell lines and normal and cancer cell lines after supplementation with glucose can be distinguished based on their unique vibrational properties. PLSDA (Partial Least Squares Discriminant Analysis) and ANOVA tests have confirmed the main role of cytochromes, proteins and lipids in differentiation of control human brain cells and cells upon supplementation with glucose. We have shown that Raman techniques combined with chemometric analysis provide additional insight to monitor the biology of astrocytes, astrocytoma and glioblastoma after glucose supplementation.

The regulation pathways of biochar and microorganism in soil-plant system by multiple statistical methods: The forms of carbon participation in coastal wetlands.

Coastal wetlands possess significant carbon storage capabilities. However, in coastal soil-plant systems augmented with biochar and microorganisms, the mechanisms of these amendments and carbon participation remain unclear. This study utilized pot experiments to explore how Enteromorpha prolifera biochar and Arbuscular mycorrhizal fungi (AMF) affect soil organic carbon (SOC), carbon-related microbes, photosynthetic and osmotic system of Suaeda salsa. The results showed biochar reduced exchangeable sodium percentage by 6.9% through adsorption and ion exchange, and increased SOC content by 34.4%. The abundance of carbon-related microorganisms (Bacteroidota and Chloroflexi) was increased and carbon metabolizing enzyme (cellulase and sucrase) activity in the soil was enhanced. AMF significantly improved plant growth compared with CK, as evidenced by the enhanced dry weight by 2.34 times. A partial least squares pathway model (PLS-PM) and correlation analysis suggested that the combined effect of biochar and AMF could be outlined as two pathways: soil and plant. Biochar increased SOC, improved the growth of soil carbon metabolizing microorganisms, and further promoted the activity of carbon-related enzymes. Additionally, AMF facilitated nutrient absorption by plants through root symbiosis, with biochar further enhancing this process by acting as a nutrient adsorber. These combined effects of biochar and AMF at soil and plant level enhanced the photosynthetic process of Suaeda salsa. The transport of photosynthetic products to the roots can increase the carbon storage in the soil. This study provides quantitative evidence supporting the increase of carbon storage in coastal wetland soil-plant systems through a combined application of biochar and AMF.

Metabolic profiling and biomarkers identification in cluster bean under drought stress using GC-MS technique.

INTRODUCTION: The Cluster bean is an economically significant annual legume, widely known as guar. Plant productivity is frequently constrained by drought conditions. OBJECTIVE: In this work, we have identified the untargeted drought stress-responsive metabolites in mature leaves of cluster beans under drought and control condition. METHODS: To analyse the untargeted metabolites, gas chromatography-mass spectrometry (GC-MS) technique was used. Supervised partial least-squares discriminate analysis and heat map were used to identify the most significant metabolites for drought tolerance. RESULTS: The mature leaves of drought-treated C. tetragonoloba cv. 'HG-365' which is a drought-tolerant cultivar, showed various types of amino acids, fatty acids, sugar alcohols and sugars as the major classes of metabolites recognized by GC-MS metabolome analysis. Metabolite profiling of guar leaves showed 23 altered metabolites. Eight metabolites (proline, valine, D-pinitol, palmitic acid, dodecanoic acid, threonine, glucose, and glycerol monostearate) with VIP score greater than one were considered as biomarkers and three metabolite biomarkers (D-pinitol, valine, and glycerol monostearate) were found for the first time in guar under drought stress. In this work, four amino acids (alanine, valine, serine and aspartic acid) were also studied, which played a significant role in drought-tolerant pathway in guar. CONCLUSION: This study provides information on the first-ever GC-MS metabolic profiling of guar. This work gives in-depth details on guar's untargeted drought-responsive metabolites and biomarkers, which can plausibly be used for further identification of biochemical pathways, enzymes, and the location of various genes under drought stress.

Strategies for multivariate characterization and classification of pulps and papers by near-infrared spectroscopy.

BACKGROUND: Multivariate calibration by Partial Least Squares (PLS) on near-infrared data has been applied successfully in several industrial sectors, including pulp and paper. The creation of multivariate calibration models relies on a set of well-characterised samples that cover the range of the intended application. However, sample sets that originate from an industrial process often show an uneven distribution of reference values. This can be addressed by curation of the reference data and the methodology for multivariate calibration. It needs to be better understood, how these approaches affect the quality and scope of the final model. RESULTS: We describe the effect of log10 transformation of the reference values, regular PLS, robust PLS, the newly introduced bin PLS, and their combinations to select more evenly distributed reference values for the quantification of five pulp characteristics (kappa number, R18, R10, cuen viscosity, and brightness; 200 samples) by near-infrared spectroscopy. The quality of the models was assessed by root mean squared error of prediction, calibration range, and coverage of sample types. The best models yielded uncertainty levels equivalent to that of the reference measurement. The optimal approach depended on the investigated reference value. SIGNIFICANCE: Robust PLS commonly gives the model with the lowest error, but this usually comes at the cost of a notably reduced calibration range. The other approaches rarely impacted the calibration range. None of them stood out as superior; their performance depended on the calibrated parameter. It is therefore worthwhile to investigate various calibration options to obtain a model that matches the requirements of the application without compromising calibration range and sample coverage.

Influence of wheat content and origin on the volatilome of craft wheat beer: An investigation by combined multivariate statistical approaches.

A deeper knowledge of the effect of wheat origin on the volatile organic compounds (VOCs) profile of craft wheat beer is crucial for its quality improvement and local product valorisation. The VOCs profile of 17 craft wheat beers obtained by common and durum, heritage and modern, wheat varieties grown in different fields sited at different altitudes was analysed. Data were processed by multivariate analysis using different approaches. Partial least square (PLS) analysis evidenced that wheat concentration was the highest source of VOCs variance, followed by, wheat species, wheat ancientness, and altitude of cultivation. An insight into the effect of wheat concentration was given by sparse PLS analysis (sPLS). The effect of wheat variety was explored by linear discriminant analysis (LDA), which permitted to correctly classify craft beers made with wheat of different origin (species and variety) on the basis of their VOCs profile. sPLS regression analysis permitted to find a combination of VOCs able to predict the altitude of wheat cultivation as well as to correctly classify wheat beers made with wheat cultivated at different altitudes. A further 'one versus all' approach by Soft Independent Modelling of Class Analogies (SIMCA) permitted to correctly authenticate beers made with different cereal species. Finally, shape analysis by generalized Procrustes analysis (GPA) revealed that the differences among samples were conserved and reflected from wheat kernels to wheat beers. This study suggests a promising use of volatiles fingerprinting with a combination of different statistical approaches to authenticate beer made with wheat of different origin and cultivated at different altitudes, thus stressing out the importance of territory in craft beer production, which, until now, was a neglected topic.

Characterization of the flavor profile of UHT milk during shelf-life via volatile metabolomics fingerprinting combined with chemometrics.

Ultra-high temperature (UHT) milk is popular among consumers. However, its flavor and texture change in its shelf life. Flavor is highly determinative for the success of dairy products and for consumers' willingness to buy. It is important to milk producers to ensure the optimal flavor of their products in the shelf life. In order to be able to control and predict the flavor quality of UHT milk during the shelf life, this study compared the variations in sensory quality, volatile aroma release and backbone flavor factors and developed a discriminant model to assess flavor quality based on flavouromics data of five competing milk sample during storage. Using partial least squares discriminant analysis (PLS-DA) with Electronic-nose (E-nose) data excellent classification sensitivity and specificity were achieved compared to models based on gas chromatography-mass spectrometry (GC-MS) data. The PLS-DA model using E-nose data exhibited a 100% correct classification rate for the storage period, and a 92% correct rate based on the eight variable importance in the projection (VIP) elements screened for volatile components from different groups. The discriminative model developed herein based on E-nose combined with chemometrics demonstrated advantages such as speed, efficiency, and environmental friendliness. This method shows promise as a precise tool for analyzing aroma changes in UHT milk during its shelf life, and provide support for controlling the flavor substances and milk product development.