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AbstractThe means of counterfeiting are becoming increasingly sophisticated, posing great challenges to detection technologies. A green method for differentiating the source of pharmaceuticals from different factories using portable near-infrared (NIR) spectroscopy and chemometrics is developed. A set of azithromycin tablets from different manufacturers are used in the experiments. The ReliefF-based algorithm and -partial least squares (PLS) are used for variable selection and modeling, respectively. Both full-spectrum and local models achieve 100% accuracy. It is more desirable to develop low-cost methods on a small wavelength range, which has been demonstrated to be a potential tool for drug quality supervision. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fish has become one of the most popular aquatic products for its beneficial effects. The quality of fish and fishery products may be influenced by their geographical origin, transportation, processing, and storage conditions. The availability of rapid and reliable techniques is important for nondestructive determination of their quality. Recently, near-infrared spectroscopy (NIRS) has been widely employed in the nondestructive evaluation of fish and fishery products. However, a comprehensive review on NIRS for this topic remains to be published. Based on this demand, the applications of NIRS in the nondestructive evaluation of fish and fishery products have been discussed in this review. This review firstly introduces the fundamentals of NIRS. Then the application of NIRS for the assessment of species, geographical origin, adulteration, freshness, nutrient components, and texture is summarized. In addition, the application of near-infrared hyperspectral imaging technology in fish and fishery products is also discussed. Finally, the challenges and prospects are outlined. The current review may provide a reference for research on NIRS in this field. In the future, NIRS could be used for online assessment of quality attributes in the fish industry through the development of new instruments and chemometrics. [ABSTRACT FROM AUTHOR]

The global burden of foodborne illnesses remains high due to consumption of contaminated food. Vigilant attention from all, particularly food handlers, is needed to address food safety concerns and minimize the risks that affect consumer health, international business, and tourism worldwide. The main objectives of this study were to: (i) evaluate the food safety knowledge, attitudes, commitment (involving affective, continuance, and normative types), and practices of students studying subjects related to food sciences in Jordanian universities and (ii) examine the impact of knowledge and attitude on commitment, and subsequently, on relevant practices using structural equation modeling. The questionnaire was completed by 526 students from 5 universities. The score percentages of students in tested categories were as follows: agreement on food safety knowledge (87.7%), attitude (92%), affective commitment (90.4%), continuance commitment (83%), normative commitment (90.3%), and practices (91.5%). The results showed that associations between food safety knowledge and attitude with the three types of commitment were positive and significant (p < 0.05). Affective and normative commitments acted as partial mediators between food safety knowledge and practices and between food safety attitude and practices, whereas continuance commitment did not act as a mediator. Furthermore, a moderate correlation was found between food safety knowledge and attitude. Focus on food safety commitment drivers is essential, as commitment plays a key role in achieving actual hygienic practices by translating adequate knowledge and positive attitudes to improvements in hygienic behavior and reductions in the risk of foodborne illnesses. [ABSTRACT FROM AUTHOR]

The quality of sugarcane in the plantation is the most important information for farmers and sugar factories for assessing the maturity of sugarcane and determining the optimal harvest schedule. The brix value is used as a quality index in the sugar industry and is an important parameter for the evaluation of cane quality and maturity. Traditional methods of determining brix involve time-consuming and labor-intensive processes, often involving destructive sampling. To overcome these challenges, this study proposes a non-destructive approach using portable near-infrared (NIR) spectroscopy to predict the sugar content in sugarcane stalks. The main objective of this study was to develop a nondestructive prediction model for the brix value in sugarcane using portable NIR spectroscopy. Data processing involved two models: Partial Least Squares (PLS) and an Artificial Neural Network (ANN), along with various data pre-treatment techniques. The PLS model showed an improvement in prediction accuracy with data pre-treatment, especially with the Savitzky-Golay method (R2 = 0.755, RMSEP = 1.22%, RMSEP = 1.43%, CV = 6.13%, and RPD = 2.02). In addition, the ANN model combined with Principal Component Analysis (PCA) showed high predictive performance when sugarcane was 11 months old (R2 = 0.797, RMSEC = 0.56%, RMSEP = 0.87%, CV = 3.04%, and RPD = 2.96). [ABSTRACT FROM AUTHOR]

Recent research on partial least squares structural equation modeling (PLS–SEM) extended the classic importance–performance map analysis (IPMA) by taking the results of a necessary condition analysis (NCA) into consideration. By also highlighting necessary conditions, the combined importance–performance map analysis (cIPMA) offers a tool that enables better prioritization of management actions to improve a key target construct. In this article, we showcase a cIPMA's main steps when using the SmartPLS 4 software. Our illustration draws on the technology acceptance model (TAM) used in the cIPMA's original publication, which features prominently in business research. [ABSTRACT FROM AUTHOR]

AbstractPork is a perishable food and often needs to be stored in the refrigerator to maintain its quality as much as possible. Traditional methods for discriminating fresh and refrigerated pork are subjective, time-consuming, or destructive. The feasibility of using near-infrared (NIR) spectroscopy combined with chemometrics was explored to discriminate fresh and refrigerated pork. A total of 104 samples including 40 fresh and 64 refrigerated samples were first prepared and split into the training and test sets. Both partial least squares (PLS) and a subspace-based ensemble algorithm were used to establish classifiers. Also, both the number of learners and the size of subspace were optimized for ensemble modeling. On the independent test set, three measures, that is, the sensitivity, specificity, and total accuracy of the ensemble classifier were 95%, 93.8%, and 94.2%, respectively, each of which is superior to that of the PLS classifier. In addition, the influence of training set composition on classifier performance was also studied, indicating that ensemble modeling is robust. The results show that the NIR spectroscopy coupled with such an ensemble model can serve as a potential tool of discriminating fresh and refrigerated pork. [ABSTRACT FROM AUTHOR]

AbstractA sensitive electrochemical nanosensor based on a glassy carbon electrode modified by carbon nanofibers (CNFs) was developed to simultaneously determine kanamycin and ampicillin. Under optimized conditions, the nanosensor showed a wide dynamic linear range (1.0 × 10−11-1.0 × 10−6 M and 1.0 × 10−10-1.0 × 10−6 M) and a low detection limit (6.12 × 10−12 M and 1.47 × 10−11 M) for kanamycin and ampicillin, respectively. The multivariable calibration of OSC-PLS was adopted to separate the obtained voltammograms. The root mean square error of prediction (RMSEPs) of kanamycin and ampicillin using the OSC-PLS model were 0.05 and 0.08, respectively. The method was used to simultaneously determine kanamycin and ampicillin in tap water. [ABSTRACT FROM AUTHOR]

Corruption has been considered as one of the significant challenges to economic growth and development worldwide. In this study, partial least squares structural equation modelling techniques were used to integrate the determinants of China's anti-corruption efforts, and their causal relationship and effects on local government expenditure and revenues during President Xi Jinping's launch of the anti-corruption campaign were assessed. As per the results of the analysis, the determinants of anti-corruption efforts were found to have a direct effect on local government expenditure and revenues determinants. [ABSTRACT FROM AUTHOR]

Understanding the fundamental light-sample interaction process is a crucial step toward the development of vibrational spectroscopy to determine fruit texture (i.e., firmness). This study aimed to investigate the effect of pectin constitution, including total pectin, water-soluble pectin, protopectin contents, and protopectin index (PI), on the optical properties and firmness of 'Baifeng' and 'Xiahui 8' peach flesh at the different softening degrees during postharvest storage of 6 days at 20 °C. The firmness of 'Baifeng' and 'Xiahui 8' peaches significantly (p < 0.05) changed with a decreasing rate from 90.3% to 92.2%. Peach firmness of these two cultivars correlated well with PI contents (r > 0.912) and showed good internal correlations with optical scattering properties. The light absorption coefficient (μa) and reduced scattering coefficient (μ's) at 600–1600 nm were measured using a single integrating sphere system combined with an inversion algorithm. This relationship of μa and μ's with peach firmness and pectin constitution was first analyzed. Notably, the specific μ's at 660 nm, 950 nm, 1203 nm, and 1453 nm showed a satisfactory prediction of peach firmness and PI of 'Xiahui 8' (R2 ≥ 0.926) and 'Baifeng' peaches (R2 ≥ 0.764), respectively. Furthermore, the prediction models were established based on partial least squares regression coupled with optical properties, and considerable prediction performances were obtained for tissue firmness (Rp2 ≥ 0.863) and PI based on μ's (Rp2 ≥ 0.802). Consequently, these results further verified that the spectroscopic prediction model for peach firmness could be related to the high correlations between PI in tissues and their optical scattering properties. Future research interests could include the development of optical absorption and scattering sensors for rapid and efficient determination of peach firmness. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has severely impacted global tourism, raising the need for sustainable recovery strategies. This study examines how tourists' confidence in tourism recovery (CTR) influences travel intentions across different pandemic phases—outbreak, stabilization, and conclusion. Positioned within the Theory of Planned Behavior (TPB), the research explores the mediating role of CTR in the relationships between attitude, subjective norms, perceived behavioral control, and travel intention. Using structural equation modeling (SEM), multi-group analysis (MGA), and Importance–Performance Matrix Analysis (IPMA), this study assesses changes in travel behavior during each pandemic phase. Data were collected through three surveys conducted in major Chinese cities between late 2022 and early 2023. The findings reveal that CTR significantly mediates the influence of behavioral factors on travel intention, especially during the stabilization and conclusion phases. The IPMA results highlight key areas for intervention, with CTR, perceived behavioral control, and subjective norms varying in importance across phases. This research underscores the need for adaptive and sustainable strategies to strengthen traveler confidence, offering practical insights for supporting long-term resilience and growth in the tourism industry during and beyond health-related crises. [ABSTRACT FROM AUTHOR]

Temperature plays a crucial role in influencing the mechanical properties of materials. Accurately measuring the temperature of devices is essential for understanding the evolution of their mechanical properties under stress and evaluating their health and lifespan. However, traditional methods encounter challenges in measuring transient temperatures and lack sufficient time-resolution capability, particularly when it comes to the rapid temperature changes at the solder interface during the switching process of power devices. In this paper, based on the close correlation between the intensities of the characteristic spectral lines of the laser-induced elements and the temperatures, a method of measuring the surface temperatures with the time-resolved capability of the order of microsecond is proposed, and a quantitative relationship between the surface temperatures of the sample and the spectral characteristics is established. The findings demonstrate that an increase in the surface temperature of the material results in enhanced intensity and signal-to-noise ratio of laser-induced plasma spectra. This enhancement is influenced by the spectral acquisition delay and gate width. To establish a quantitative relationship between surface temperature and spectral properties, back propagation-artificial neural network (BP-ANN) and partial least squares (PLS) are employed for fitting and calibration. The fitted models can achieve linear correlation coefficient indexes exceeding 0.99. Notably, the BP-ANN fitted model exhibites a small fitting bias, with a root mean squared error (RMSE) of 2.582 and a correctness rate of 98.3%. The method provides an effective means for transient temperature measurement of objects and gives a strong support for the assessment of the health status of the soldering interface of power devices.

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, β-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, β-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. [ABSTRACT FROM AUTHOR]

EVOO is widely considered a superior edible oil due to its unique taste and aroma. Its consumption is associated with several health benefits, many of which are attributed to its phenolic compounds. This work involved the analysis of 203 EVOO samples using the official method recommended by the IOC (COI/T.20/Doc. No 29/Rev.1 2017) to determine their polyphenolic content. In addition, ultraviolet (UV) absorption spectroscopy has been applied to the same EVOO samples in combination with PLS regression to assess the polyphenolic content. This work demonstrates that ultraviolet absorption spectroscopy offers an efficient and cost-effective alternative. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AbstractA portable device for digital image colorimetry (DIC) is reported for the determination of Ca in crude oil emulsions. ASTM D4807 was used for sample preparation for the isolation of saline species containing Ca. DIC was evaluated based on the complexation reactions with Ca using calcein, murexide, and calcon. Partial least squares (PLS) provided the best determination coefficients (higher than 0.990) for all complexing reagents. Significant interference of Mg was observed for the determination of Ca using calcein. PLS models with the lowest root mean square error of cross validation (RMSECV) values were obtained for murexide and calcon for a concentration of 0.250 mmol L−1 at pH of 13. The best conditions for camera focal length were 11 and 2 mm for murexide and calcon. For light intensity, the optimized conditions for murexide and calcon was 180 lux. Stabilities of color complexes over the time were 30 and 15 min for murexide and calcon, respectively. DIC/murexide provided the lowest root mean square error of prediction (RMSEP) and a linear response was observed from 0.5 to 10 mg L−1 Ca and the relative standard deviation (RSD) was lower than 10%. Significant differences were not observed between the results obtained by DIC/murexide and those obtained by microwave-assisted wet digestion (MAWD) with determination by inductively coupled plasma-optical emission spectrometry (ICP-OES). A limit of quantification of 0.50 mg kg−1 Ca were obtained using DIC/murexide. This approach is an alternative and useful method for the determination of Ca in crude oil samples, with advantages of miniaturization, low cost, and ease of operation. [ABSTRACT FROM AUTHOR]

To reveal the inter-relationships between the strategic axes of the analyzed company, we used the Balanced Scorecard Analysis (BSC) 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.

Fish has become one of the most popular aquatic products for its beneficial effects. The quality of fish and fishery products may be influenced by their geographical origin, transportation, processing, and storage conditions. The availability of rapid and reliable techniques is important for nondestructive determination of their quality. Recently, near-infrared spectroscopy (NIRS) has been widely employed in the nondestructive evaluation of fish and fishery products. However, a comprehensive review on NIRS for this topic remains to be published. Based on this demand, the applications of NIRS in the nondestructive evaluation of fish and fishery products have been discussed in this review. This review firstly introduces the fundamentals of NIRS. Then the application of NIRS for the assessment of species, geographical origin, adulteration, freshness, nutrient components, and texture is summarized. In addition, the application of near-infrared hyperspectral imaging technology in fish and fishery products is also discussed. Finally, the challenges and prospects are outlined. The current review may provide a reference for research on NIRS in this field. In the future, NIRS could be used for online assessment of quality attributes in the fish industry through the development of new instruments and chemometrics.

Stable boundary layer height (SBLH) is an important parameter to characterize the characteristics and vertical structure of the nocturnal lower atmosphere at night. The distribution of SBLH has obvious spatial and temporal differences, and there are many meteorological factors affecting the SBLH, but at present, there are few quantitative studies on the effects of near-surface meteorological factors on the SBLH in the desert hinterland. This study was based on GPS sounding balloon data, near-surface meteorological observation data, and ERA5 data from Tazhong Station (TZ) in the Taklamakan Desert (TD) collected in July 2017, 2019, and 2021. The variation characteristics of the SBLH and its relationship with near-surface meteorological factors are described. We quantitatively analyzed the degree of influence of near-surface meteorological factors affecting the SBLH and verified it using a model. The study also elucidates the possible formation mechanism of the SBLH in the TD hinterland. The SBLH in the TD hinterland trended upward in July 2017, 2019, and 2021, which is consistent with the changes in meteorological factors, according to the near-surface meteorological observation and ERA5 data. Therefore, we think that an inherent connection exists between near-surface meteorological factors and the SBLH. The results of correlation analysis show that complex internal connections and interactions exist among the meteorological factors near the ground; some thermal, dynamic, and other meteorological factors strongly correlate with the SBLH. Having established the change in SBLH (ΔSBLH) and in major thermal, dynamic, and other meteorological factors (Δ), the linear regression equation between them revealed that near-surface meteorological factors can affect the SBLH. The dynamic factors have a stronger influence on the ΔSBLH than thermal and other factors. The results of model validation based on the variable importance projection (VIP) also confirmed that the SBLH in the TD hinterland is jointly affected by dynamic and thermal factors, but the dynamic factors have a stronger impact. The mechanism through which the SBLH forms is relatively complex. At night, surface radiative cooling promotes the formation of a surface inversion layer, and low-level jets strengthen wind shear, reducing atmospheric stability. The combined effects of heat and dynamics play an important role in dynamically shaping the SBLH. This study helps us with accurately predicting and understanding the characteristics of the changes in and the factors influencing the SBLH in the TD hinterland, providing a reference for understanding the mechanism through which the SBLH forms in this area. At the same time, it provides a scientific basis for regional weather and climate simulation, meteorological disaster defense, air quality forecasting, and model parameterization improvement. [ABSTRACT FROM AUTHOR]

First-line tuberculostatic agents, Rifampicin (RIF), Isoniazid (ISH), Ethambutol (ETB), and Pyrazinamide (PZA) are generally administered as a fixed-dose combination (FDC) for improving patient adherence. The major quality challenge of these FDC products is their variable bioavailability, where RIF and its solid state are key factors. In this work, the analysis of the impact of the polymorphism in the performance of RIF in RIF-ISH and PZA-RIF-ISH combined products was carried out by an overall approach that included the development and validation of two methodologies combining near-infrared (NIR) spectroscopy and partial least squares (PLS) to the further evaluation of commercial products. For NIR-PLS methods, training and validation sets were prepared with mixtures of Form I/Form II of RIF, and the appropriate amount of ISH (for double associations) or ISH-PZA (for triple associations). The corresponding matrix of the excipients was added to the mixture of APIs to simulate the environment of each FDC product. Four PLS factors, reduced spectral range, and the combination of standard normal variate and Savitzky-Golay 1st derivative (SNV-D') were selected as optimum data pre-treatment for both methods, yielding satisfactory recoveries during the analysis of validation sets (98.5±2.0%, and 98.7±1.8% for double- and triple-FDC products, respectively). The NIR-PLS model for RIF-ISH successfully estimated the polymorphic purity of Form II in double-FDC capsules (1.02 ± 0.02w/w). On the other hand, the NIR-PLS model for RIF-ISH-PZA detected a low purity of Form II in triple FDC tablets (0.800 ± 0.021w/w), these results were confirmed by X-ray powder diffraction. Nevertheless, the triple-FDC tablets showed good performance in the dissolution test (Q=99-102%), implying a Form II purity about of 80% is not low enough to affect the safety and efficacy of the product. [ABSTRACT FROM AUTHOR]

In this work, solid-surface fluorescence spectra of Rhodamine B (RhB) and fluorescein (FLU), two extensively used fluorophores, were studied and their concentrations were semi-quantitatively determined using the three-dimensional excitation–emission matrix (3D-EEM) data. In order to measure the solid fluorescence spectra, a trace amount of fluorophores was physically adsorbed on a piece of filter paper as a fluorescent paper, and the fluorescence spectra were recorded from the surface of the fluorescent paper instead of the inner parts of the solution. The spectral measurement was performed utilizing a routine spectrofluorimeter designed to measure the fluorescence of solution only by placing the fluorescent paper in a simple handmade holder at an adjusted incident angle of radiation. The results revealed a significant increase in the fluorescence intensity of the fluorescent paper strip compared to the solution phase. Moreover, in the RhB–FLU paper strip the forster resonance energy transfer was observed between FLU and RhB, similar to the observed phenomenon in the solution phase. For quantifying fluorophores, the EEM data were successfully resolved by applying multivariate partial least squares and parallel factor analysis (PARAFAC) modeling, and the results were compared with the univariate method. This study may provide a general guideline in further developing the solid fluorescence measurement of concentrated solutions or solid samples that have fluorescence measurement limitations such as high self-absorption and highly scattered spectra by using a conventional spectrofluorimeter, and also of biological samples where a trace amount of the analyte is available and high sensitivity is required. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) is a topic of growing interest. While many researchers have identified factors that influence satisfaction with VR, additional important factors remain uninvestigated. In our research model, system quality, presence, and authenticity influence two mediating variables of enjoyment and usefulness of information. Enjoyment (a hedonic aspect of the VR experience) and usefulness of information (a utilitarian aspect), in turn, influence satisfaction, with familiarity moderating both of these relationships. PLS analysis of survey data collected in a heritage tourism context finds relationships that have not been previously identified. While system quality and authenticity are associated with enjoyment and usefulness of information, presence is positively associated only with enjoyment. Familiarity negatively moderates the relationship between enjoyment and satisfaction with VR. This study thus extends prior research on a key metaverse technology, VR, by identifying and explicating the roles of authenticity and familiarity—and also extends prior research by focusing on the hedonic and utilitarian dimensions of the VR experience. [ABSTRACT FROM AUTHOR]

Running has gained popularity as a fitness activity in China, with a growing number of young runners utilizing wearable devices to monitor their running routines and engage in quantified self-practices. The continuous evolution of wearable devices in terms of products and services has expanded the choices available to young runners. Therefore, there is a need to analyze the factors influencing the continuance intention of young runners, providing insights into how to promote the sustained growth of these products or services in the market. This study is grounded in the Technology Acceptance Model and the Theory of Planned Behavior, with an extension incorporating the quantified self to explore the impact of users' continuance intentions to use wearable devices. A survey was conducted among 468 young runners who already used wearable devices, and the data collected were analyzed using PLS-SEM. The results indicate that perceived usefulness and attitudes from the Technology Acceptance Model positively influence intentions for continued use. Additionally, subjective norms according to the Theory of Planned Behavior positively influence continuance use intentions. However, perceived behavioral control does not have a significant effect on continuance use intentions. Conversely, the Quantified-Self positively influences continuance use intentions and partially mediates the relationship between perceived usefulness and continuance use intentions. This research has several theoretical implications for the Theory of Planned Behavior, the Technology Acceptance Model, and the Quantified-Self research construct. Moreover, this study has practical implications for practitioners concerning the adoption and acceptance of wearable devices by young people. This approach enables practitioners to target and implement precise strategies to meet the current demands of the young runner market. Doi: 10.28991/HIJ-2023-04-04-02 Full Text: PDF

Understanding the fundamental light-sample interaction process is a crucial step toward the development of vibrational spectroscopy to determine fruit texture (i.e., firmness). This study aimed to investigate the effect of pectin constitution, including total pectin, water-soluble pectin, protopectin contents, and protopectin index (PI), on the optical properties and firmness of ‘Baifeng’ and ‘Xiahui 8’ peach flesh at the different softening degrees during postharvest storage of 6 days at 20 °C. The firmness of ‘Baifeng’ and ‘Xiahui 8’ peaches significantly (p < 0.05) changed with a decreasing rate from 90.3% to 92.2%. Peach firmness of these two cultivars correlated well with PI contents (r > 0.912) and showed good internal correlations with optical scattering properties. The light absorption coefficient (μa) and reduced scattering coefficient (μ’s) at 600–1600 nm were measured using a single integrating sphere system combined with an inversion algorithm. This relationship of μa and μ’s with peach firmness and pectin constitution was first analyzed. Notably, the specific μ’s at 660 nm, 950 nm, 1203 nm, and 1453 nm showed a satisfactory prediction of peach firmness and PI of ‘Xiahui 8’ (R2 ≥ 0.926) and ‘Baifeng’ peaches (R2 ≥ 0.764), respectively. Furthermore, the prediction models were established based on partial least squares regression coupled with optical properties, and considerable prediction performances were obtained for tissue firmness (Rp2 ≥ 0.863) and PI based on μ’s (Rp2 ≥ 0.802). Consequently, these results further verified that the spectroscopic prediction model for peach firmness could be related to the high correlations between PI in tissues and their optical scattering properties. Future research interests could include the development of optical absorption and scattering sensors for rapid and efficient determination of peach firmness.

Abstract Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

Nitro-aromatic compounds (NACs) pose a significant risk to human health and other living organisms. In this paper, a novel ZnS:Mn2+ quantum dots/N-methylpolypyrrole (ZnS:Mn2+ QDs/NMPPy) hybrid fluorescence probe was synthesized and applied for the simultaneous determination of NACs. Multivariate chemometric methods, including principal component analysis and partial least squares (PLS), were used for analyses of response of sensor to different NACs. The sensing behavior of the hybrid QDs/polymer probe was investigated for common NACs in aqueous media. The PLS model was built under the optimum conditions for the simultaneous determination of a set of NACs. The descriptive and predictive power of the PLS models was assessed using the calculated values of root mean square errors (RMSE) and coefficient of determination (R2). The RMSE and R2 values were in the ranges of 0.01–0.96 and 0.95–0.99 for the training and test set samples, respectively. The linearity was estimated in the concentration range of 0.50–15.00 μM with the detection limit of 481.04, 293.15, 345.47, 458.13, and 131.79 nM for NB, NT, NP, DNP, and TNP, respectively. The results revealed that the designed optical sensor combined with chemometric algorithms could alleviate the problem of spectral overlapping of analytes and provide promising results for the simultaneous determination of NACs without a need for pre-extraction and separation in mixtures and complex media of seawater as actual samples. [ABSTRACT FROM AUTHOR]

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Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a rapid and non-destructive method based on Fourier transform near infrared (FT-NIR) to predict gastrodin content in fresh Gastrodia elata. Auto-ordered predictors selection (autoOPS) and successive projections algorithm (SPA) were applied to select the most informative variables related to gastrodin content. Based on that, partial least squares regression (PLSR) and multiple linear regression (MLR) models were compared. The autoOPS-SPA-MLR model showed the best prediction performances, with the determination coefficient of prediction ( R p 2 ), ratio performance deviation (RPD) and range error ratio (RER) values of 0.9712, 5.83 and 27.65, respectively. Consequently, these results indicated that FT-NIRS technique combined with chemometrics could be an efficient tool to rapidly quantify gastrodin in Gastrodia elata and thus facilitate quality control of Gastrodia elata. [ABSTRACT FROM AUTHOR]

The present bottleneck in biosimilar bioprocess development has become evaluation of analytical results, due to recent advances in analytics, such as automated sample preparation and development of high‐throughput methods. Currently automated chromatogram integration and annotation is only efficient for simple chromatograms. In an ever more competitive field of biosimilars, this represents a serious drawback because chromatographic analytical methods that provide some of the most valuable physicochemical quality attributes of the product also require careful chromatogram integration and annotation. This work focuses on the glycan mapping analytical method as utilized in development of monoclonal antibody biosimilars, evaluating more than 2000 chromatograms spanning the life cycle of multiple biosimilar development projects. It proposes a modified workflow by implementing automatic machine learning algorithms to determine the proportion of specific relevant glycan species in a sample directly from the chromatogram. Data preparation and analysis is performed using a pipeline approach. Pipeline is a modular design of data processing where signal "travels" through various active modules in a series. Each module performs a specific function or transformation on the signal and propagates the transformed signal to the next module. The pipeline is designed in a way that modules can be independently improved and exchanged. Module functions currently implemented are chromatogram resampling by spline interpolation, baseline removal by asymmetric least squares, peak alignment using parametric time warping, and quantification of the relative proportion of a glycan species using partial least squares regression. Hyper‐parameters of the pipeline are then optimized using the Nelder–Mead method. The approach stands out for its ability to accommodate a broad landscape of samples, covering multiple different proteins in different stages of biosimilar development, analyzed using different adaptations of the glycan map analytical method. The pipeline presents an intuitive, flexible, and creatively simple method design capable of providing reliable results for a wide range of glycan species essential for biosimilar development. It enables transparent, faster, and less subjective evaluation of analytic raw data (from sample to result). Furthermore, our automated approach maintained an accuracy comparable with manual integration thus demonstrating its readiness for implementation in the conservative and highly regulated environment. The presented methodology reduces the cost and time of biosimilar development and should be applicable for any chromatogram‐based analytical method. The current analytical workflow for biosimilar development is modified by implementing automatic machine learning algorithms that can determine the proportion of specific relevant glycan species in a sample directly from the chromatogram. [ABSTRACT FROM AUTHOR]

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, β-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, β-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.

This paper explores the prediction of the soluble solid content (SSC) in the visible and near-infrared (400–1,000 nm) regions of Baise mango. Hyperspectral images of Baise mangoes with wavelengths of 400–1,000 nm were obtained using a hyperspectral imaging system. Multiple scatter correction (MSC) was chosen to remove the effect of noise on the accuracy of the partial least squares (PLS) regression model. On this basis, the characteristic wavelengths of mango SSC were selected using the competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), uninformative variable elimination (UVE), and combined CARS + GA-SPA, CARS + UVE-SPA, and GA + UVE-SPA characteristic wavelength methods. The results show that the combined MSC-CARS + GA-SPA-PLS algorithm can reduce redundant information and improve the computational efficiency, so it is an effective method to predict the SSC of mangoes. [ABSTRACT FROM AUTHOR]

The application and accumulation of phosphogypsum (PG) may cause soil pollution, so it is of significance to establish a rapid method for its determination in soil. In this study, the feasibility of quantifying PG in soil by multivariate calibration combined with portable near-infrared spectroscopy (NIR) and infrared spectroscopy (IR) was investigated. In order to obtain better accuracy, standard normal variable (SNV) and Savitzky-Golay smoothing were employed as the pretreatment methods for IR and NIR, respectively. The competitive adaptive reweighted sampling (CARS) algorithm was used for variable optimization of these models. The results show that the predictive determination coefficient and root mean square error of prediction (RMSEP) of IR and NIR partial least squares (PLS) models were 0.9933 and 1.88% and 0.8830 and 6.55%. The limits of detection (LOD) for the models were 4.0006% and 14.225%. The reproducibility of the models is satisfactory with good accuracy and precision. In addition, extreme learning machine (ELM) and support vector machine (SVM) algorithms were also used to analyze the data, resulting in similar outcomes to those obtained by PLS. The results of a dual t test demonstrated that there is no significant difference between these methods and the standard procedure (GB/T 23456-2018) at the 95% confidence level. However, the reported protocols have the advantages of on-site analysis, speed, and convenience for the determination of phosphogypsum in soil. [ABSTRACT FROM AUTHOR]

Over the course of the last twenty years, building information modeling (BIM) has emerged as a firmly established construction methodology integrating fundamental principles. The implementation of BIM methodologies possesses the capability to augment the attainment of quality, cost, and schedule objectives in construction endeavors. Notwithstanding the widespread adoption of BIM in the construction sector, the execution of BIM-related tasks frequently suffers from the absence of established methodologies. The objective of this study was to create a BIM application model through an examination of the correlation between BIM integration and the achievement of overall project success (OPS) in construction endeavors. In order to develop the BIM application model, feedback was solicited from a cohort of fourteen industry experts who assessed a range of BIM activities in light of prior research. The data that were gathered underwent exploratory factor analysis (EFA) in order to authenticate the results acquired from the expert interviews. Furthermore, construction professionals participated in structured surveys in order to evaluate the importance of said BIM practices. This study utilized partial least squares–structural equation modeling (PLS-SEM) to ascertain and authenticate the underlying framework and correlations between BIM implementation and OPS. The findings indicate a moderate correlation between the implementation of BIM and the success of a project wherein BIM is responsible for approximately 52% of the project's overall success. To optimize project outcomes, it is recommended that construction companies prioritize the implementation of BIM practices. This study highlights the correlation between the utilization of BIM and favorable project results, emphasizing the necessity for the construction sector to adopt BIM as a revolutionary instrument to attain enhanced project achievements. [ABSTRACT FROM AUTHOR]

Relative radiometric normalization (RRN) is a widely used method for enhancing the radiometric consistency among multitemporal satellite images. Diverse satellite images enhance the information for observing the Earth’s surface and bring additional uncertainties in the applications using multisensor images, such as change detection, multitemporal analysis, and image fusion. To address this challenge, we developed a multirule-based RRN method for multisensor satellite images, which involves the identification of spectral- and spatial-invariant pseudo-invariant features (PIFs) and a partial least-squares (PLS) regression-based RRN modeling using neighboring target pixels around PIFs. The proposed RRN method was validated on four datasets and demonstrated excellent effectiveness in identifying high-quality PIFs with spectral- and spatial-invariant properties, estimating precise regression models, and enhancing the radiometric consistency of reference–target image pair. Our method outperformed six RRN methods and effectively processed well-registered medium- and high-resolution images from the same sensor. This letter highlights the potential of our method for generating more comparable bitemporal multisensor images. [ABSTRACT FROM AUTHOR]

Vegetation phenology is considered an essential biological indicator in understanding the behavior of ecosystems and how they respond to environmental cues. However, the potential of interannual variations of remotely sensed phenology signals to differentiate plant types remains poorly understood, especially in understudied systems with highly heterogeneous landscapes such as wetlands. This study presents a case study in a San Francisco Bay area marsh that investigates the usefulness of interannual variation, defined as the root-mean-square error of enhanced vegetation index (EVI) measurements against a fitted phenology curve, at the beginning, middle, and end of the growing season as indicators of plant types. The study found that altitude above sea level and certain land surface phenology metrics, such as the day-of-the-year of the end of the season, the mid-autumn day, and the greening rate before the summer peak, were significantly related to these interannual variation trends. These results indicate that a detailed time-series analysis at the beginning and end of growing seasons may enhance large-scale wetland characterization. Overall, the findings of this study contribute to our understanding of vegetation phenology and provide a framework for more accurate wetland classification in future studies. [ABSTRACT FROM AUTHOR]

The feasibility of raw data acquired by liquid chromatography – high resolution mass spectrometry for the separation of four tequila corresponding to different maturation stages was demonstrated using partial least squares regression (PLS). Five samples of silver, rested, aged, and extra-aged tequilas from different manufacturers were analyzed each in two technical replicates. Prior to the analysis, the samples were four-fold diluted with 0.1% trifluoroacetic acid. Rectangle bucketing was performed for raw data, with a retention time width of 60 s and m/z width of 1. The prepared dataset was submitted to principal component analysis (PCA) which allowed for the detection of tequila contaminants identified to be N-lauryl diethanolamine and phthalate esters. Next, PLS was performed on depurated data, annotating tequila categories as follows: 0 - silver, 1 - rested, 2 - aged and 3 - extra aged. In the computed model, two principal components accounted for 64% and 98% of X-data and Y-data variability, respectively. Cross-validation revealed similar analytical performance in calibration and in prediction with root mean square errors of 0.1851 and 0.2082, respectively. The assignation of category's numerical value (0-3) was attained for eight randomly selected tequilas (two per category). Statistical differences between predicted values were obtained for each pair of categories except for the aged versus extra-aged liquors (ANOVA, p < 0.05). Overall, the assignation of tequila category was achieved with no need for the detection/identification nor quantification of individual compounds. [ABSTRACT FROM AUTHOR]

Gram-negative bacteria had been regarded as several important sources of lethal infection. Rapid identification of pathogenic bacteria is extremely important for the diagnosis and clinical treatment of diseases. In current study, three gram-negative bacteria, including Klebsiella aerogenes, Enterobacter cloacae and Escherichia coli, were used to access the feasibility of characterizing Gramnegative bacteria by surface-enhanced Raman Spectroscopy (SERS). Bacterial samples were from Escherichia coli isolates (n=1000), Klebsiella aerogenes isolates (n=1000) and Enterobacter cloacaeand isolates (n=1000). The differences of three Gram-negative bacteria were characterized by SERS spectra. Furthermore, four multivariate statistical algorithms based on the combination of principal component analysis (or partial least squares) and linear discriminant analysis (or support vector machine) were used to discriminate the spectra of three gramnegative bacteria. [ABSTRACT FROM AUTHOR]