Partial Least Squares Model Research Articles

Extraction process optimization and link evaluation of Reyanning Mixture based on quality by design idea

Reyanning Mixture is one of the superior Chinese patent medicine varieties of "Qin medicine". Based on the idea of quality by design(QbD), the extraction process of the Reyanning Mixture was optimized. The caffeic acid, polydatin, resveratrol, and emodin were used as critical quality attributes(CQAs). The material-liquid ratio, extraction temperature, and extraction time were taken as critical process parameters(CPPs) by the Plackett-Burman test. The mathematical model was established by the star design-effect surface method, and the design space was constructed and verified. The optimal extraction process of the Reyanning Mixture was obtained as follows: material-liquid ratio of 11.84 g·mL~(-1), extraction temperature at 81 ℃, and two extractions. A partial least-square(PLS) quantitative model for CQAs was established by using near-infrared spectroscopy(NIRS) combined with high-performance liquid chromatography(HPLC) under the optimal extraction process. The results showed that the correlation coefficients of the correction set(R_c) and validation set(R_p) of the quantitative models of four CQAs were more than 0.9. The root mean square error of the correction set(RMSEC) were 0.744, 6.71, 3.95, and 1.53 μg·mL~(-1), respectively, and the root mean square error of the validation set(RMSEP) were 0.709, 5.88, 2.92, and 1.59 μg·mL~(-1), respectively. Therefore, the optimized extraction process of the Reyanning Mixture is reasonable, feasible, stable, and reliable. The NIRS quantitative model has a good prediction, which can be used for the rapid content determination of CQAs during extraction. They can provide an experimental basis for the process research and quality control of Reyanning Mixture.

An optimised and validated surrogate analyte A-TEEM-PARAFAC-PLS technique for detecting and quantifying the biological oxygen demand in surface water.

A 5-day test duration makes BOD5 measurement unsatisfactory and hinders the development of a quick technique. Protein-like fluorescence peaks show a strong correlation between the BOD characteristics and the fluorescence intensities. For identifying and measuring BOD in surface water, a simultaneous absorbance-transmittance and fluorescence excitation-emission matrices (A-TEEM) method combined with PARAFAC (parallel factor) and PLS (partial least squares) analyses was developed using a tyrosine and tryptophan (tyr-trpt) mix as a surrogate analyte for BOD. The use of a surrogate analyte was decided upon due to lack of fluorescent BOD standards. Tyr-trpt mix standard solutions were added to surface water samples to prepare calibration and validation samples. PARAFAC analysis of excitation-emission matrices detected the tyr-trpt mix in surface water. PLS modelling demonstrated significant linearity (R2 = 0.991) between the predicted and measured tyr-trypt mix concentrations, and accuracy and robustness were all acceptable per the ICH Q2 (R2) and ASTM multivariate calibration/validation procedures guidelines. Based on a suitable and workable surrogate analyte method, these results imply that BOD can be detected and quantified using the A-TEEM-PARAFAC-PLS method. Very positive comparability between tyr-trypt mix concentrations was found, suggesting that tyr-trypt mix might eventually take the place of a BOD-based sampling protocol. Overall, this approach offers a novel tool that can be quickly applied in water treatment plant settings and is a step in supporting the trend toward rapid BOD determination in waters. Further studies should demonstrate the wide application of the method using real wastewater samples from various water treatment facilities.

Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach.

The global demand for protein is on an upward trajectory, and peanut protein powder has emerged as a significant player, owing to its affordability and high quality, with great future market potential. However, the industry currently lacks efficient methods for rapid quality testing. This research paper addressed this gap by introducing a portable device with employed near-infrared spectroscopy (NIR) to quickly assess the quality of peanut protein powder. The principal component analysis (PCA), partial least squares (PLS), and generalized regression neural network (GRNN) methods were used to construct the model to further enhance the accuracy and efficiency of the device. The results demonstrated that the newly established NIR method with PLS and GRNN analysis simultaneously predicted the fat, protein, and moisture of peanut protein powder. The GRNN model showed better predictive performance than the PLS model, the correlation coefficient in calibration (Rcal) of the fat, the protein, and the moisture of peanut protein powder were 0.995, 0.990, and 0.990, respectively, and the residual prediction deviation (RPD) were 10.82, 10.03, and 8.41, respectively. The findings unveiled that the portable NIR spectroscopic equipment combined with the GRNN method achieved rapid quantitative analysis of peanut protein powder. This advancement holds a significant application of this device for the industry, potentially revolutionizing quality testing procedures and ensuring the consistent delivery of high-quality products to fulfil consumer desires.

IDENTIFYING THE INTER-RELATIONSHIPS BETWEEN THE COMPANY'S STRATEGIC SECTORS

To reveal the inter-relationships between the strategic axes of the analyzed company, we used the Balanced Scorecard Analysis (BSC)1 and the Partial Least Squares (PLS) technique. The PLS technique identifies strategic areas (or axes) by grouping performance indicators that have the highest percentage of explaining the variance within the indicator group. We linked the strategic axes with the company's performance sectors, while the PLS model illustrated the interaction between the company's strategic lines as well as the interaction between the company's performance indicators and their respective groups. We started with principal component analysis (PCA) to identify the most important activity sectors for the company (for example, axis 1 = Profitability), to which we associated between 4 and 9 relevant performance indicators and selected those that were most correlated with their respective strategic sectors. We used the PLS technique for principal component analysis to show the correlations between the company's strategic sectors. The intensity of the relationships within the company allowed us to predict potential strategic lines for improving managerial performance. The BSC enabled us to explain the relationship between corporate governance variables and company performance. Within the causal relationships, we provided a logical explanation of how the analyzed business sectors are interconnected.

Use of Constant-Analyte Samples in Updating Quantitative Near-Infrared Calibration Models for Physiological Levels of Glucose

Updating procedures were investigated for multivariate calibration models for physiological levels of glucose in aqueous samples based on near-infrared spectroscopy. The methodology evaluated used samples with constant glucose concentrations collected on the prediction day to augment previously collected calibration spectra for the purpose of computing an updated model for use only on that day. Models were based on partial-least squares (PLS) regression in conjunction with spectral preprocessing using direct orthogonal signal correction (DOSC). The use of constant-analyte samples allowed the procedure to be compatible with samples collected at the beginning of a chemical reaction or from a fasting patient in a blood glucose monitoring application. The methodology was tested with nine sets of prediction spectra collected from 368 to 617 days after the collection of the calibration data. The updated PLS models based on four augmented samples exhibited an approximately 50% improvement in prediction accuracy relative to models that were based solely on the original calibration data. The use of DOSC in conjunction with updating was found to be beneficial overall, but only led on average to less than 10% improvement in prediction performance.

Quantitative image analysis for assessing extracellular polymeric substances in activated sludge under atrazine exposure

Extracellular polymeric substances (EPS) play a vital role in biological wastewater treatment systems. This study investigates the impact of herbicide atrazine (ATZ) on the overall performance, EPS yield, composition, and sludge morphology in an activated sludge (AS) system operated in a sequencing batch reactor (SBR). Since conventional methods for analyzing EPS are time-consuming and releases residues, a new approach was developed in this work to evaluate the EPS fractions and components, based on the morphological characterization of the biomass using quantitative image analysis (QIA) technique coupled with multivariate statistics.Results showed that exposure to ATZ inhibit biomass activity in terms of organic matter (COD) and nitrogen removal. Moreover, both tightly bound EPS (TB-EPS) and loosely bound EPS (LB-EPS) increased under ATZ, indicating that microorganisms release EPS as a defense mechanism against environmental changes. The PN/PS ratio also increases, indicating likely increased hydrophobicity in ATZ phases. Furthermore, ATZ phases exhibit a predominance of large aggregates compared to intermediate and small ones, indicating a change in aggregate morphological structure associated with EPS production. The new approach using QIA coupled with partial least squares (PLS) modeling provides accurate predictions of EPS content. The increase in TB-EPS is closely related to the rise of large aggregates in phases exposed to higher ATZ concentrations. The PLS models demonstrate high accuracy for EPS prediction (coefficients of determination, R2 above 0.86), showcasing the feasibility of using QIA for EPS assessment in AS systems. This approach offers significant potential for regular process monitoring and management, providing a more environmentally friendly methodology by eliminating the need for chemical usage.

Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats

We have developed a quantitative safety prediction model for subchronic repeated doses of diverse organic chemicals on rats using the novel quantitative read-across structure-activity relationship (q-RASAR) approach, which uses similarity-based descriptors for predictive model generation. The experimental -Log (NOAEL) values have been used here as a potential indicator of oral subchronic safety on rats as it determines the maximum dose level for which no observed adverse effects of chemicals are found. A total of 186 data points of diverse organic chemicals have been used for the model generation using structural and physicochemical (0D–2D) descriptors. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D–2D descriptors. Then, the combined pool of RASAR and the identified 0D–2D descriptors of the training set were employed to develop the final models by using the partial least squares (PLS) algorithm. The developed PLS model was rigorously validated by various internal and external validation metrics as suggested by the Organization for Economic Co-operation and Development (OECD). The final q-RASAR model is proven to be statistically sound, robust and externally predictive (R2 = 0.85, Q2LOO = 0.82 and Q2F1 = 0.94), superseding the internal as well as external predictivity of the corresponding quantitative structure-activity relationship (QSAR) model as well as previously reported subchronic repeated dose toxicity model found in the literature. In a nutshell, the q-RASAR is an effective approach that has the potential to be used as a good alternative way to improve external predictivity, interpretability, and transferability for subchronic oral safety prediction as well as ecotoxicity risk identification.

Near-infrared spectroscopy in resin transfer molding—determination of the degree of cure

Near-infrared (NIR) spectroscopy is employed to directly monitor the degree of cure of composite material during the curing phase in the resin transfer molding (RTM) process. The composite material consists of natural fibers and an epoxy-amine resin system. The quantitative determination of the degree of cure, α, from the NIR spectra is realized by partial least square (PLS) regression in conjunction with various pre-treatments of the spectral data. As a reference, the degree of cure is determined by isothermal differential scanning calorimetry (DSC) experiments. The best PLS model that could be obtained for α is characterized by a determination coefficient of prediction (R²P) of 0.980 and root mean square error of prediction (RMSEP) of 4.4 %. While the spectra change during curing according to the literature for each RTM trial, significant differences can be observed between the spectra of different RTM trials. The reasons for this are discussed in detail. The findings show the potential of inline NIR spectroscopy for monitoring α in the RTM process.

Comparative study of linear and nonlinear calibration algorithm for extrapolation ability of near infrared spectroscopy quantitative analysis

The determination of the o-nitrotoluene (o-MNT) content in separation process of mononitrotoluene (MNT) is of interest, since it affects the purity of m-nitrotoluene (m-MNT) and p-nitrotoluene (p-MNT). In real-world applications, the calibration model inevitably requires dealing with complex extrapolation problems. Therefore, this study extracted the spectral features of the o-nitrotoluene based on the interval selection algorithm. The linear calibration method (partial least squares (PLS)) and nonlinear calibration methods (support vector machine (SVM), back propagation (BP), random forest (RF), extreme learning machine (ELM)) were used to build the calibration models based on o-nitrotoluene samples in different concentration ranges, and the prediction accuracy and robustness of the calibration model were compared. The results indicate that the effectiveness of different calibration methods is different when going from prediction accuracy to robustness. The prediction accuracy and robustness of RF models are not satisfactory. BP models, which are capable of producing very accurate results in terms of prediction accuracy, are not able to solve extrapolation problems. PLS model has more advantages in model prediction accuracy. ELM has shown the best behavior in terms of robustness of model, but is inferior to PLS in terms of prediction accuracy.

The ice recrystallization inhibition activity of wheat glutenin hydrolysates and effect of salt on their activity

This study aimed to investigate the antifreeze activity of wheat glutenin hydrolysates produced by Alcalase and trypsin at varying degrees of hydrolysis. The influence of salt concentrations in two dispersing media, phosphate-buffered saline (PBS) and NaCl solution, and the types of cations, NaCl and CaCl2, on hydrolysates was further analyzed. The results revealed a consistently higher ice recrystallization inhibition (IRI) activity for all hydrolysates dispersed in 0.1 x PBS than in 1 x PBS. Interestingly, glutenin hydrolysates exhibited better IRI activity in CaCl2 than in NaCl solution under the same ionic strength. Furthermore, trypsin-derived hydrolysates (with a molar mass about 10 kDa) consistently had better IRI activity than those produced by Alcalase (with a molar mass less than 1 kDa), regardless of the dispersing media. The observed increase in IRI activity was associated with alterations in the secondary structure, characterized by a higher α-helix and lower β-turns content. This suggests that structural rigidity of the molecules plays a significant role in enhancing the antifreeze activity of peptides. Additionally, the scores plot of the partial least-squares discriminant analysis model indicates that salt concentration had a more significant impact than salt type on the IRI activity of glutenin hydrolysates. In conclusion, our findings provide insights into the factors influencing the IRI activity of protein hydrolysates.

Effect of Green Transformational Leadership on Organizational Environmental Performance: The Role of Green Human Resource Management Practices (Study on SMEs in The Embroidery Sector)

Every organization must actively engage in environmental sustainability efforts by incorporating practices that prioritize the careful selection and maintenance of eco-friendly inputs, processes, and outputs within their business operations. However, empirical research on the specific influence of Green Transformational Leadership (GTL) on Organizational Environmental Performance is lacking, particularly within the Small Medium Enterprises (SMEs) of the embroidery sector in Indonesia. This study aims to address this gap by examining the direct and indirect effects of GTL, utilizing Green Human Resources Management (GHRM) practices as a potential mediator in the context of Indonesian SMEs specializing in embroidery. The research, conducted on 300 employees from 150 SMEs in the prominent embroidery industry of 20 city/regency areas in East Java, employs a Structural Equation Model of Partial Least Squares (SEM-PLS). Findings show a statistically significant positive influence of GTL on Organizational Environmental Performance directly, with GHRM practices partially mediating this relationship. Bridging this research gap not only contributes to academic knowledge but also offers practical insights for organizations seeking to enhance their environmental sustainability.

Inversion Tillage Combined with Organic Fertilizer Application Increased Maize Yield via Improving Soil Pore Structure and Enzymatic Activity in Haplic Chernozem

Inversion tillage and organic fertilizer application can break the plow pan and improve soil quality. However, the effects of combining these practices on the soil microbial resource limitation and maize yield in Haplic Chernozem are unclear. In this research, a field experiment was established in 2018, and soil samples were collected in 2021 in Longjiang County in Northeast China, which is a Haplic Chernozem region. Four treatments comprising conventional tillage (T15), conventional tillage with organic fertilizer (T15+M), inversion tillage (T35), and inversion tillage with organic fertilizer (T35+M) were randomly arranged with four replications. Compared with T15 and T15+M treatments, soil bulk density significantly decreased by 11.1–16.3% in the 15–35 cm layer under T35 and T35+M treatments, accompanied by the improvement in soil pore structure (e.g., soil porosity, circularity, and Euler number). T15+M treatment significantly increased soil organic carbon and soil nutrient contents by 11.1–16.3% and 3.9–24.5% in the 0–15 cm layer compared with other treatments. However, soil organic carbon, total nitrogen, available phosphorus content, microbial biomass, and enzymatic activities reached the maximum values in the 0–35 cm layer under T35+M treatment. In addition, T35+M treatment had the highest maize yield and sustainable yield index. Extracellular enzymatic stoichiometry suggested that soil microorganisms are generally co-limited by carbon and phosphorus in Haplic Chernozem. However, T35+M treatment significantly reduced soil microbial resource limitation, which was one important factor impacting maize yield and sustainability. Random-forest and partial least-squares path modeling showed that T35+M treatment could reduce soil microbial resource limitation and increase the stability and sustainability of the maize yield by improving soil available nutrients, microbial biomass, and pore structure. Therefore, the incorporation of inversion tillage and organic fertilizer is a suitable soil management practice in view of increasing soil quality and crop yields in a Haplic Chernozem region.

Real-time model correction using Kalman filter for Raman-controlled cell culture processes

Raman spectroscopy has found extensive use in monitoring and controlling cell culture processes. In this context, the prediction accuracy of Raman-based models is of paramount importance. However, models established with data from manually fed-batch cultures often exhibit poor performance in Raman-controlled cultures. Thus, there is a need for effective methods to rectify these models. The objective of this paper is to investigate the efficacy of Kalman filter (KF) algorithm in correcting Raman-based models during cell culture. Initially, partial least squares (PLS) models for different components were constructed using data from manually fed-batch cultures, and the predictive performance of these models was compared. Subsequently, various correction methods including the PLS-KF-KF method proposed in this study were employed to refine the PLS models. Finally, a case study involving the auto-control of glucose concentration demonstrated the application of optimal model correction method. The results indicated that the original PLS models exhibited differential performance between manually fed-batch cultures and Raman controlled cultures. For glucose, the RMSEP of manually fed-batch culture and Raman-controlled culture was 0.23 and 0.40 g·L-1. With the implementation of model correction methods, there was a significant improvement in model performance within Raman-controlled cultures. The RMSEP for glucose from updating-PLS, KF-PLS, and PLS-KF-KF was 0.38, 0.36, and 0.17 g·L-1, respectively. Notably, the proposed PLS-KF-KF model correction method was found to be more effective and stable, playing a vital role in the automated nutrient feeding of cell cultures.

Inversion of soil carbon, nitrogen, and phosphorus in the Yellow River Wetland of Shaanxi Province using field in situ hyperspectroscopy

Soil nitrogen and phosphorus are directly related to soil quality and vegetation growth and are, therefore, a common research topic in studies on global climate change, material cycling, and information exchange in terrestrial ecosystems. However, collecting soil hyperspectral data under in situ conditions and predicting soil properties, which can effectively save time, manpower, material resources, and financial costs, have been generally undervalued. Recent optimization techniques have, however, addressed several of the limitations previously restricting this technique. In this study, hyperspectral data were taken from surface soils under different vegetation types in the wetlands of the Shaanxi Yellow River Wetland Provincial Nature Reserve. Through in situ original and first-order differential transformation spectral data, three prediction models for soil carbon, nitrogen, and phosphorus contents were established: partial least squares (PLSR), random forest (RF), and Gaussian process regression (GPR). The R2 and RMSR of the constructed models were then compared to select the optimal model for evaluating soil content. The soil organic carbon, total nitrogen, and total phosphorus content models established based on the first-order differential had a higher accuracy when modeling and during model validation than those of other models. Moreover, the PLSR model based on the original spectrum and the Gaussian process regression model had a superior inversion performance. These results provide solid theoretical and technical support for developing the optimal model for the quantitative inversion of wetland surface soil carbon, nitrogen, and phosphorus based on in situ hyperspectral technology.

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy.

Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky-Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (Rc2) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (Rp2) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9-3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.

Rapid quantitative analysis of cotton-polyester blended fabrics using near-infrared spectroscopy combined with CNN-LSTM

Accurate and rapid quantitative analysis of fiber content is important for blended fabric quality control and supervision. In this paper, a deep learning method based on an improved hybrid network of convolutional neural network (CNN) and long short-term memory (LSTM) to fast and accurate determine cotton content of cotton-polyester blended fabrics using near-infrared (NIR) spectroscopy. The cotton-polyester blended fabric samples were partitioned into training set, validation set and testing set in the ratio of 6:2:2 to develop the quantitative model of cotton content of blended fabric. Firstly, a one-dimensional CNN architecture is designed and modeled to obtain the CNN parameters and to evaluate the spectral preprocessing methods. Then, the CNN structure is modified and improved using LSTM to obtain a CNN-LSTM structure. The root mean square error (RMSE) of the validation set obtained by CNN model is 0.83%, which is 34.03% lower than that of the traditional partial least squares (PLS) model, and the RMSE of the testing set obtained by CNN model is 0.88%, which is 25.88% lower than that of the PLS model. The RMSE of the validation set obtained by CNN-LSTM model is 0.65%, which is 47.85% lower than that of the PLS model, and the RMSE of the testing set obtained by CNN-LSTM model is 0.75%, which is 36.57% lower than that of the PLS model. Compared with PLS and CNN models, the CNN-LSTM model can more accurately analyze the cotton content proportion of cotton-polyester blended fabrics, which provides a new fast and non-invasive method for the quantitative detection of fabrics.

Nutrient quantification of livestock slurry and digestate using a low-cost handheld NIR spectrometer and multi-biomass calibration models

Real-time analysis during slurry field application can potentially ensure higher efficiency in crop fertilisation and reduce related environmental problems. Near Infrared Spectroscopy (NIRS) can predict quickly and adequately the nutrient content of the slurry but the devices are still costly. In this study, a low-cost portable device has been tested to predict total solids, total Kjeldahl nitrogen, total ammonia nitrogen, and phosphorus contents in cattle and pig slurries and digestate. Multivariate calibration models were developed for different biomass types (multi-biomass) by comparing support vector machine (SVM) and partial least square (PLS) regressions. The developed SVM and PLS models showed satisfactory and similar results, with performance to deviation ratio (RPD) values ranging between 2.22 and 2.80. The use of the handheld device with the obtained models also meets the requirements of a certification protocol for commercial NIR sensors and therefore can ensure a better slurry management at farm level.

Forecasting wine phenolic composition from infrared spectra of grapes extracts and monitoring of fermentations with optimised time-specific prediction models

Incorporating monitoring and forecasting technologies has the potential to enhance the different aspects of winemaking. By monitoring certain chemical parameters, it is possible to evaluate the progress of the fermentation and phenolic extraction, while also detecting deviations from expected behaviour. This study aims to investigate the development of a rapid forecasting method and different strategies to improve the accuracy of process monitoring. The first part involves the introduction of partial least squares (PLS) calibration models able to forecast the trajectories of phenolic extraction during fermentation using grape extract spectra. Additionally, novel methods for reoptimizing calibration models are proposed, with the intention of increasing the prediction accuracy. Whilst this method is transferable to PLS models, principal component regression (PCR) models were used instead as a more simplistic way to test the new methods. First, dynamic PCR linear regression coefficients were adapted over time to capture the compound evolution in the wine matrix. Secondly, PCR calibration models were reoptimized using interpolated spectral and reference data. The forecasting PLS calibration models have shown promising results for total anthocyanin content (mg/L), colour density, polymeric pigments (mg/L), MCP tannins (mg/L), and total phenolic index with overall R2 values on the validation set above 0.6, and low RSMEV values. Using interpolated data to re-optimise models was found to be more effective than global models or those using the calculated regression coefficients methods for predicting the concentration of a particular compound at a certain time point. While R2 values are suboptimal for all considered PCR models, in 72% of the cases analysed, the interpolation method for re-optimisation showed RMSEV values between 6% and 83% lower than those reported for a more traditional PCR model and other re-optimisation strategies. Interpolated models have shown consistency in predicting values, along with good performance metrics, indicating that they could be applied to samples for which calibration models do not exist.

Improve the accuracy of FT-NIR for determination of zearalenone content in wheat by using the characteristic wavelength optimization algorithm

The research contributes a unique method to achieve high-precision quantification of zearalenone (ZEN) in wheat, significantly improving accuracy in the analysis. Fourier transform near infrared spectroscopy (FT-NIR) was employed to capture the spectral information of wheat with different mildew degrees. Three feature selection models, competitive adaptive reweighted sampling (CARS), support vector machine-recursive feature elimination (SVM-RFE), and multiple feature-spaces ensemble-least absolute shrinkage and selection operator (MFE-LASSO) were introduced to processed pre-processed near-infrared spectral data and established partial least squares (PLS) regression according to the selected features. The outcomes indicated that the optimal generalization performance was achieved by the PLS model optimized through the MFE-LASSO model. The root mean square error of prediction (RMSEP) was 18.6442 μg·kg-1, coefficient of predictive determination (RP2) was 0.9545, and relative percent deviation (RPD) was 4.3198. According to the results, it is feasible to construct a stoichiometric model for the quantitative determination of ZEN in wheat by using FT-NIR combined with feature selection algorithm, and this method can also be extended to the detection of various molds in other cereals in the future.

Modelling of beer sensory staleness based on flavour instability parameters

Why was the work done: The deterioration of the flavour of fresh beer is challenging for the brewing industry. Despite extensive research on flavour instability, the focus has centred on a limited set of parameters, rather than taking a broader approach.
 How was the work done: in this study, the intent was to produce a flavour stable beer. Using a low kilning temperature, malt of low staling potential was used in combination with best brewing practice to produce three batches of unpasteurised top fermented pale beers at a pilot scale. Forty-three markers were analysed in the fresh and aged beers (30°C for 15, 30, 60 and 90 days). Staleness was evaluated by a trained sensory panel and multivariate data analysis was used to explore how the markers contribute to staleness.
 What are the main findings: Repeatability was achieved between replicate brews and, subsequently, staling. Polyphenols, haze, total reactive antioxidant potential (TRAP), iso-α-acids, colour, furfural, 2-methylpropanal and 2-methylbutanal showed a strong correlation with staleness. Staleness doubled after 60 days of storage at 30°C, despite volatile aldehydes remaining below their sensory thresholds, implying a synergistic effect of carbonyls contributing to staleness. A Partial Least Square (PLS) model was established, modelling the sensory staleness from 2-methylpropanal, furfural, TRAP and the trans-/cis-iso-α-acids ratio.
 Why is the work important: The staling phenomena could be reproduced in beers from parallel brewing trials with only minor variations. The four parameters in PLS modelling indicate that beer staling involves a combination of oxidative and non-oxidative pathways.