Fourier Transform Near-infrared Spectroscopy Research Articles

Characterisation of Low Molecular Weight Compounds of Strawberry Tree (Arbutus unedo L.) Fruit Spirit Aged with Oak Wood

There is a trend towards the commercialisation of strawberry tree fruit spirit (AUS) with wood ageing, motivated by its favourable sensory characteristics. Additionally, further studies are necessary to elucidate the optimal conditions regarding ageing time and toasting level. This study evaluated the changes in colour and low molecular weight compounds (LMWC) of AUS aged for three and six months using oak wood (Quercus robur L.) with light, medium and medium plus toasting levels. For this purpose, phenolic acids (gallic, ellagic, ferulic and syringic acids), phenolic aldehydes (vanillin, syringaldehyde, coniferaldehyde and sinapaldehyde) and furanic aldehydes (furfural, 5-hydroxymethylfurfural and 5-methylfurfural) were quantified using the HPLC method. Chromatic characteristics, colour sensory analysis and total polyphenol index were also analysed. Fourier transform near-infrared spectroscopy (FT-NIR) was used to discriminate between samples. The results emphasized the favourable effect of oak wood contact on enhancing the colour and enriching AUS with low molecular weight compounds (LMWC). AUS aged in medium toasted wood exhibits high levels of total phenolic index, 5-hydroxymethylfurfural, furfural, coniferaldehyde, sinapaldehyde, sum LMWC and chromatic characteristics b* and C. Concentrations of syringaldehyde, ellagic acid, vanillin and syringic acid and a lighter colour (a* chromaticity coordinates) are higher in AUS aged with slightly more toasted wood. Nearly all analysed parameters showed an increase with ageing time. The FT-NIR technique allowed for the differentiation of aged AUS, focusing more on ageing time than on toasting level.

Prediction of Retinol in Fortified Maize Flour using Fourier Transform - Near Infrared Spectroscopy

Food fortification is one strategy for addressing micronutrient deficiencies among the population groups at risk. Non-compliance with fortification standards hinders the success of fortification programs. This is due to a lack of techniques to rapidly check the amounts of the added fortificants. Fourier transform - near-infrared (FT-NIR) spectroscopy is a fast and reliable technique that would be used to ensure adherence to requirements. This study aimed to investigate the potential of using FT-NIR spectroscopy to predict the amount of retinol in fortified maize flour. 150 fortified maize flour samples were used in this study. Partial least squares regression (PLS-R) was used to build calibration models based on the retinol reference values obtained by high-performance liquid chromatography (HPLC), and fortified maize flour NIR spectra acquired from the FT-NIR spectrophotometer. Two calibration models were developed to predict retinol above and below 1.0 mg/kg. The performance metrics of model one developed to predict retinol < 1.0 mg/kg were: R2c = 0.81, RMSEE = 0.08, RPD = 2.29 and R2v = 0.82, RMSEP = 0.09, RPD = 2.07 for the calibration and validation, respectively. The second model developed to predict retinol ≥ 1.0 mg/kg had the following performance metrics: R2c = 0.93, RMSEE = 0.16, RPD = 3.58 and R2v = 0.81, RMSEP = 0.22, RPD = 2.43 for the calibration and validation, respectively. Overall, the findings demonstrated that FT-NIR spectroscopy can be utilised to reliably predict retinol levels in fortified maize flour samples. FT-NIR spectroscopy, by replacing time-consuming and laborious wet chemistry laboratory procedures, has the potential to be used for rapid regulatory monitoring of fortification compliance for a large number of samples.

Rapid and cost‐effective assessment of nutrients in pistachio (Pistacia vera L.) leaves through Fourier transform near‐infrared spectroscopy (FT‐NIRS)

AbstractBackgroundPistachio nuts provide many health benefits in human diet. Nutrient levels in plant leaves and fertilizer schedules are determined based on traditional soil and leaf chemical analyses. However, these methods require additional labor, time, and cost, which is why most farmers do not prefer them and cannot detect nutrient deficiencies in time. Fast, easy‐to‐use and low‐cost nutrient level assessment techniques are needed.AimThis study aims to explore the viability of near‐infrared reflectance spectroscopy (NIRS) as a fast, user friendly, and cost effective technique for evluating the major macro‐ and micronutrient contents of dried and ground pistachio leaf sample.MethodsThe feasibility of NIRS for estimating nutrient contents was investigated by analyzing samples from 200 pistachio trees. Dried and ground pistachio leaves were subjected to NIRS analysis. PLSR (Partial least square regression) analyses were performed to develop nutrient content prediction models using spectral information of samples.ResultsIt was found that the NIRS system had a very good potential to estimate the K, Ca, Cu, and Mg contents of the leaf samples (R2 > 0.80). It was also found that Fe and Mn concentrations could also be estimated with good accuracy (R2 = 0.70–0.80). However, the NIRS system did not give good results for the prediction of N, P, and Zn (R2 < 0.40).ConclusionIn conclusion, the NIRS system can be used to quickly, easily, and economically assess the K, Ca, Cu, Mg, Fe, and Mn contents of dried and ground pistachio leaves. This technique has the potential to improve nutrient management practices in pistachio farming within a sustainable and environmentally conscious framework. Fourier transform NIRS (FT‐NIRS) can provide valuable insights by complementing rather than replacing traditional chemical analysis. Laboratory analysis is still required for definitive nutrient content information, but FT‐NIRS can significantly reduce the reliance on such analysis, thereby mitigating the environmental impact caused by the excessive use of chemical fertilizers and minimizing the health risks to laboratory staff. In addition, the rapid information‐gathering capabilities of the FT‐NIRS can be emphasized.

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.

Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics

Paw San rice, also known as “Myanmar pearl rice”, is considered the highest quality rice in Myanmar. There are considerable differences in terms of the premium commercial value of Paw San rice, which is an incentive for fraud, e.g. adulteration with cheaper rice varieties or mislabelling its geographical origin. Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive. Therefore, there is a need for rapid, robust and cost-effective analytical techniques for monitoring the authenticity and geographical origin of Paw San rice.In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice.The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition.

Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

Application of Infrared Spectroscopy in the Identification of Fallen Trees from the Amazon Rainforest (Myristicaceae)

Abstract The infrared spectrum carries the so-called fingerprint of a plant, a characteristic that can help the identification of wood from an integrative perspective. Fourier-transform near-infrared spectroscopy (FT-NIRS) was evaluated for its potential use in the taxonomic identification of natural fallen trees from the Amazon rainforest (Myristicaceae). Trees found on the forest floor (Amazonas, Brazil) were surveyed, and eight individuals were selected for the study. The material was removed from the trunk for anatomical identification based on xylem structure and obtaining NIR spectra (10,000 to 4,000 cm-1). A total of 1,080 spectra were collected and used in the multivariate analysis. Absorption bands in the NIR region and chemometrics (calibration models) were used to interpret the spectra, which indicated similarity trends at the genus and species levels. Linear discriminant analysis (LDA) was able to differentiate Iryanthera ulei and Virola (five species), which were misclassified in the field as a single species (Virola sp.). The accuracy of the results obtained by chemometric models (LDA 7 and 14) indicates the robustness of NIR spectroscopy, which can be used as a new tool in the integrative taxonomy of plant material from tropical forests that are difficult to identify and can analyzing many tests quickly and securely. Study Implications: The taxonomic identification of natural fallen trees found on the floor of tropical forests is quite complex given the lack of flowers, fruits, and leaves in field collection. Fourier-transform near-infrared spectroscopy demonstrated its capability to distinguish between wood samples from fallen trees belonging to the Myristicaceae family. The chemometric model successfully discriminated the samples, which had been erroneously lumped together in the field under the single species designation of Virola sp. This technique has been previously overlooked for providing differentiation. Our study highlights its efficacy to address misclassifications and allow taxonomic identification.

Rapid testing in the food industry: the potential of Fourier transform near-infrared (FT-NIR) spectroscopy and spatially offset Raman spectroscopy (SORS) to detect raw material defects in hazelnuts (Corylus avellana L.)

The detection of raw material defects, e.g., due to incorrect or excessively long storage, is an important issue in incoming goods inspections in the food industry. Fast and easy-to-use analytical methods for evaluating the usability of raw materials are particularly important. In this study, the applicability of Fourier transform near-infrared (FT-NIR) spectroscopy and spatially offset Raman spectroscopy (SORS) for the detection of raw material defects was evaluated. For this purpose, six hazelnut batches stored at different temperatures, humidity levels, and storage times were used as examples in this pilot study. Classification models of samples before and after the different physical treatments show that the resulting changes can be detected by FT-NIR spectroscopy and SORS at elevated temperature and humidity. When one of the storage parameters is increased, FT-NIR spectroscopy is also useful for detecting differences between sample groups. In contrast, SORS cannot distinguish between pre- and post-stored samples when only one of the storage parameters is increased, making SORS unsuitable for incoming inspection of nuts. FT-NIR spectroscopy analysis is also a fast application, because freeze-drying of the sample material prior to analysis is not required as the results before and after freeze-drying are comparable. Combining the FT-NIR spectroscopy and SORS data in a low-level data fusion improved the classification models for samples stored at low storage temperatures, suggesting that the two methods provide complementary information. In summary, analyzing nuts with FT-NIR spectroscopy and SORS, as shown for hazelnuts, has the potential to identify abnormal samples during incoming goods inspections.

Rapid and Non-Destructive Determination of Fatty Acid Profile and Oil Content in Diverse Brassica carinata Germplasm Using Fourier-Transform Near-Infrared Spectroscopy

Brassica carinata is one of the oilseeds in the Brassicaceae family, possessing seed quality traits such as oil with various fatty acid profiles suitable for many industrial applications. Determination of such quality traits using conventional methods is often expensive, time-consuming, and destructive. In contrast, the Near-Infrared Spectroscopic (NIRS) technique has been proven fast, cost-effective, and non-destructive for the determination of seed compositions. This study aimed to demonstrate that NIRS is a rapid and non-destructive method for determining the fatty acid profile and oil content in diverse germplasms of B. carinata. A total of 96 genetically diverse B. carinata germplasms that include accessions, advanced breeding lines, and varieties were used in this study. Reference data sets were generated using gas chromatography and the Soxhlet oil extraction method for fatty acid profile and oil content, respectively. Spectra data were taken from the wavenumber range of 11,500 to 4000 cm−1 using the Fourier-transform near-infrared (FT-NIR) method. NIRS calibration equations were developed using partial least square (PLS) regression with OPUS software, version 7.5.1. Higher coefficient of determination (R2val) and ratio of performance to deviation (RPD) > 3 were obtained for oleic acid (R2val = 0.92, RPD = 3.6), linoleic acid (R2val = 0.89, RPD = 3.2), linolenic acid (R2val = 0.93, RPD = 3.8), erucic acid (R2val = 0.92, RPD = 3.5), and oil content (R2val = 0.93, RPD = 3.6). Thus, the NIRS calibration models for the aforementioned fatty acids and oil content were found to be strong enough for prediction. However, the calibration models for palmitic acid (R2val = 0.78, RPD = 2.1) and stearic acid (R2val = 0.75, RPD = 2.0) showed relatively smaller R2val and thus became weaker in their prediction capacity. Despite their relatively lower R2, the calibration equations for palmitic and stearic acids could be used for approximate estimation and rough screening purposes. In conclusion, the calibration models that we have developed will be useful in applying NIRS as a high-throughput, non-destructive method for the screening of large germplasms in terms of their fatty acid profiles and oil content during the oil quality breeding efforts conducted on B. carinata.

Investigation of Electronic Structures of Triplet States Using Step-Scan Time-Resolved Fourier-Transform Near-Infrared Spectroscopy.

Triplet transitions of light-emitting materials, including rose bengal, tris(2-phenylpyridine)iridium(III) [Ir(ppy)3], tris(1-phenylisoquinoline)iridium(III) [Ir(piq)3], and bis[2-(4,6-difluorophenyl)pyridinato-C2,N](picolinato)iridium(III) (FIrpic), were studied using step-scan time-resolved Fourier-transform near-infrared spectroscopy. The samples were excited to their singlet excited states by a 355 nm laser and then underwent efficient conversions/crossings to their triplet manifolds. For rose bengal, a transient absorption band appeared at 9400 cm-1, attributed to the T3 ← T1 transition based on the corresponding time evolution and the theoretical calculations. For Ir(ppy)3, Ir(piq)3, and FIrpic, the most intense bands were observed at 7700, 7500, and 7500 cm-1 and assigned to T7 ← T1, T6 ← T1, and T6 ← T1 transitions, respectively. For Ir(ppy)3, the most intense band involved transitions between different triplet metal-to-ligand charge transfer (3MLCT) states, while for Ir(piq)3 and FIrpic, they involved a metal center to 3MLCT transition. These T1 states were assigned to 3MLCT.

Rapid detection of soil carbonates by means of NIR spectroscopy, deep learning methods and phase quantification by powder X-ray diffraction

In this study we propose a novel rapid and efficient way to predict carbonates content in soil by means of Fourier Transform Near-Infrared (FT-NIR) reflectance spectroscopy and by use of deep learning methods. In addition to using traditional machine learning algorithms, we exploited multiple deep learning methods, such as: 1) a Multi-Layered Perceptron Regressor (MLP) and 2) a Convolutional Neural Network (CNN) in an attempt to compare their performance with other classical machine learning algorithms, which up until now were considered the field’s standards, such as Partial Least Squares Regression (PLSR), Cubist and SupportVectorMachines (SVM) on the combined dataset of two NIR spectral libraries: Kellogg Soil Survey Laboratory (KSSL) of the United States Department of Agriculture (USDA), a dataset of soil samples reflectance spectra collected nationwide, and Land Use and Coverage Area Frame Survey (LUCAS) TopSoil-2015 (European Soil Library) which contains soil sample absorbance spectra from all over the European Union, and use them to predict carbonate content on never-before-seen soil samples. In this study, absorbances in the NIR spectral region (1150-2500 nm) were utilized in two different ways: a) as one-dimensional data and b) as two-dimensional spectrograms which, to our knowledge, is a completely novel approach that has rarely been researched. Quantification of carbonates by means of X-ray-Diffraction is in good agreement with the volumetric method and the MLP prediction. Our work contributes to rapid carbonates content prediction in soil samples in cases where: 1) no volumetric method is available and 2) only NIR spectra absorbance data are available.

A Novel Variable Selection Method Based on Ordered Predictors Selection and Successive Projections Algorithm for Predicting Gastrodin Content in Fresh Gastrodia elata Using Fourier Transform Near-Infrared Spectroscopy and Chemometrics.

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 (Rp2), 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.

Optimization of Fourier transform near-infrared spectroscopy model in determining saponin compounds of Panax notoginseng roots

As a traditional Chinese medicine, Panax notoginseng (Burk.) F.H.Chen (P. notoginseng) is abundant in chemical compounds, particularly the high content of saponin compounds, which have been extensively implemented in clinical treatment. The traditional chemical methods have drawbacks of destroying samples and taking a long time to analyze the saponin compounds content. In this study, we investigated the viability of employing Fourier transform near infrared spectroscopy (FT-NIR) to assess the saponin compounds content of P. notoginseng rapidly. The partial least squares regression (PLSR) prediction model was established based on spectral information from 252 samples. The effects of various variable selection methods, including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), uninformative variables elimination (UVE), and correlation coefficients (Correlation) on the model performance, were compared. One examined variable selection algorithm that stood out was the correlation coefficient method. The Correlation-PLSR model’ calibration and prediction sets had a high coefficient of determination (Rc2: 0.966-0.989; Rp2: 0.968-0.999) and low root mean square error (RMSEC: 1.293-5.984; RMSEP: 0.291-1.810). It was indicated it can rapidly predict saponin compounds in P. notoginseng. This study offers a rapid and reliable quantitative method for P. notoginseng quality control.

A Comparative Study of Fourier Transform Near-Infrared Spectroscopy and Physicochemical Methods in Wheat Quality Analysis

Near-infrared spectroscopy is a non-invasive, rapid, and efficient analytical method widely employed in agricultural quality assessment. This study aims to explore the potential of Fourier transform near-infrared (FT-NIR) spectroscopy in the analysis of wheat quality in comparison to traditional physicochemical methods. Key quality parameters such as protein content, bulk density, dough extensibility, development and stability time, wet gluten, and extension area were considered. To compare the similarities between the two methods in an irregular manner and to make the trials more representative, randomized combinations of different samples were made by planting different wheat genotypes in different locations. In wheat protein determination (r = 0.92, p < 0.001), the correlation between protein determinations was very high and highly statistically significant. This indicates that the Fourier transform near-infrared (FT-NIR) assay is highly consistent and accurate with the physicochemical method for protein determination. In the quality indices of bulk density (r = 0.46, p < 0.05), dough extensibility (r = 0.46, p < 0.05), development time (r = 0.37, p < 0.05), stability time (r = 0.49, p < 0.01), wet gluten (r = 0.54, p < 0.01), and dough extension area (r = 0.63, p < 0.001), there was a positive correlation between the two assays. Although the correlation was lower compared to the protein assay, it was still statistically significant. This suggests that the FT-NIR assay also has some accuracy in these indices, although there may be some differences from the physicochemical method. Through comparative analysis, we found that in wheat quality assessment, FT-NIR showed a strong correlation and was highly significant in the determination of wheat protein content; in the determination of the extension area, although highly significant, the correlation coefficient was not high, and there was a positive correlation between the two, and a lower correlation was shown in the bulk density, dough extensibility, and development time. These results indicate that FT-NIR can assess the wheat protein content quality indicator. However, its ability to accurately assess wheat quality indicators such as density, dough extensibility, development and stability time, wet gluten, and dough extensibility needs further investigation.

HPLC-DAD, GC–MS, UHPLC-Q-TOF/MS2, ICP-MS and FT-NIR combined with the chemometrics explain seasonal variation in Cassia senna L. leaves from the coast of Red Sea in Sudan

Crops reveal considerable capability to regulate their phytochemicals and trace element contents to seasonal changes in temperature and rainfalls. In this study, we have measured the secondary metabolites, volatiles, and trace elements of Cassia senna L leaves collected from the Red Sea State, Sudan during winter 2018, summer 2019, and autumn 2020. Chemical profiles were evaluated by High-performance liquid chromatography with diode-array detection (HPLC-DAD), Ultra-high performance liquid chromatography (UHPLC); Inductively coupled plasma mass spectrometry(ICP-MS), Gas chromatography-mass spectrometry (GC–MS), Fourier-transform near-infrared spectroscopy (FT-NIR). 46 metabolites, 33 volatile compounds, and 13 elements were reported. The findings suggested significant seasonal variations in sennoside (A-B) compounds, which were reported to have positive health effects. In this study, aloesin was identified for the first time in senna leaves. Analysis of seasonal variations was obtained by principal component analysis and orthogonal partial least squares discriminant model. Additionally, chemical markers for each harvest season were reported. Overall, the number of metabolites and volatiles identified as a function of the season was autumn > summer> winter. Our results have enhanced the knowledge of the selection of harvesting time, with the best characteristics for the intended therapeutic application.

The Rapid Detection of Trash Content in Seed Cotton Using Near-Infrared Spectroscopy Combined with Characteristic Wavelength Selection

Herein, we propose a new method based on Fourier-transform near-infrared spectroscopy (FT-NIR) for detecting impurities in seed cotton. Based on the spectral data of 152 seed cotton samples, we screened the characteristic wavelengths in full-band spectral data with regard to potential correlation with the trash content of seed cotton. Then, we applied joint synergy interval partial least squares (siPLS) and combinatory algorithms with the competitive adaptive reweighted sampling method (CARS) and the successive projection algorithm (SPA). In addition, we used the sparrow search algorithm (SSA), gray wolf algorithm (GWO), and eagle algorithm (BES) to optimize parameters for support vector machine (SVM) analysis. Finally, the feature wavelengths optimized via the six feature wavelength extraction algorithms were modeled and analyzed via partial least squares (PLS), SSA-SVM, GWO-SVM, and BES-SVM, respectively. The correlation coefficients, Rc and Rp, of the calibration and prediction sets were subsequently used as model evaluation indices; comparative analysis highlighted that the preferred option was the inverse estimation model as this could accurately predict the trash content of seed cotton. Subsequently, we found that the accuracy of predicting the content of impurities in seed cotton when applying the optimized SVM model of SSA combined with the feature wavelengths screened via siPLS-SPA was optimal. Thus, the optimal modeling method for inverse impurity content was siPLS-SPA-SSA-SVM, with an Rc value of 0.9841 and an Rp value of 0.9765. The rapid application development (RPD) value was 6.7224; this is >3, indicating excellent predictive ability. The spectral inversion model for determining the impurity rate of mechanized harvested seed cotton samples established herein can, therefore, determine the impurity rate in a highly accurate manner, thus providing a reference for the subsequent construction of a portable spectral detector of impurity rate. This will help objectively and quantitatively characterize the impurity rate of mechanized harvested seed cotton and provide a new tool for rapidly detecting impurities in mechanized harvested wheat. Our findings are limited by the small sample size and the fact that the model developed for estimating the impurity content of seed cotton was specific to a local experimental field and certain varieties of cotton.

Comparison of a Nanofiber-Reinforced Composite with Different Types of Composite Resins.

The aim of this study was a comprehensive evaluation and comparison of the physical and mechanical properties of a newly developed nano-sized hydroxyapatite fiber-reinforced composite with other fiber-reinforced and particle-filled composites. Commercially available eight composite resins (3 fiber-reinforced and 5 particle-filled) were used: Fiber-reinforced composites: (1) NovaPro Fill (Nanova): newly developed nano-sized hydroxyapatite fiber-reinforced composite (nHAFC-NF); (2) Alert (Pentron): micrometer-scale glass fiber-reinforced composite (µmGFC-AL); (3) Ever X Posterior (GC Corp): millimeter-scale glass fiber-reinforced composite (mmGFC-EX); Particle-filled composites: (4) SDR Plus (Dentsply) low-viscosity bulk-fill (LVBF-SDR); (5) Estelite Bulk Fill (Tokuyama Corp.) low-viscosity bulk-fill (LVBF-EBF); (6) Filtek Bulk Fill Flow (3M ESPE) low-viscosity bulk-fill (LVBF-FBFF); (7) Filtek Bulk Fill (3M ESPE) high-viscosity bulk-fill (HVBF-FBF); and (8) Filtek Z250 (3M ESPE): microhybrid composite (µH-FZ). For Vickers microhardness, cylindrical-shaped specimens (diameter: 4 mm, height: 2 mm) were fabricated (n = 10). For the three-point bending test, bar-shaped (2 × 2 × 25 mm) specimens were fabricated (n = 10). Flexural strength and modulus elasticity were calculated. AcuVol, a video image device, was used for volumetric polymerization shrinkage (VPS) evaluations (n = 6). The polymerization degree of conversion (DC) was measured on the top and bottom surfaces with Fourier Transform Near-Infrared Spectroscopy (FTIR; n = 5). The data were statistically analyzed using one-way ANOVA, Tukey HSD, Welsch ANOVA, and Games-Howell tests (p < 0.05). Pearson coefficient correlation was used to determine the linear correlation. Group µH-FZ displayed the highest microhardness, flexural strength, and modulus elasticity, while Group HVBF-FBF exhibited significantly lower VPS than other composites. When comparing the fiber-reinforced composites, Group mmGFC-EX showed significantly higher microhardness, flexural strength, modulus elasticity, and lower VPS than Group nHAFC-NF but similar DC. A strong correlation was determined between microhardness, VPS and inorganic filler by wt% and vol% (r = 0.572-0.877). Fiber type and length could affect the physical and mechanical properties of fibers containing composite resins.

Quantification of ciprofloxacin in pharmaceutical products from various brands using FT-NIR: A comparative investigation of PLS and MCR-ALS

This study aims to quantify ciprofloxacin in commercial tablets with varying excipient compositions using Fourier Transform Near-Infrared Spectroscopy (FT-NIR) and chemometric models: Partial Least Squares (PLS) and Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS).Matrix variation, arising from differences in excipient compositions among the tablets, can impact quantification accuracy. We discuss this phenomenon, emphasizing potential issues introduced by varying certain excipients and its importance in reliable ciprofloxacin quantification.We evaluated the performance of PLS and MCR-ALS models independently on two sets of tablets, each containing the same drug substance but different excipients. The statistical results revealed promising results with PLS prediction error of 0.38% w/w of the first set and 0.47% w/w of the second set, while MCR-ALS achieved prediction errors of 0.67% w/w of the first set and 1.76% w/w of the second set.To address the challenge of matrix variation, we developed single models for PLS and MCR-ALS using a dataset combining both first and second sets. The PLS single model demonstrated a prediction error of 4.3% w/w and a relative error of 6.41% w/w, while the MCR-ALS single model showed a prediction error of 1.88% w/w and a relative error of 1.29% w/w.We then assessed the performance of the single PLS and MCR-ALS models developed based on the combination of the first and the second set in quantifying ciprofloxacin in various commercial tablet brands containing new excipients. The PLS model achieved a prediction error ranging between 6.2% w/w and 8.39% w/w, with relative errors varied between 8.53% w/w and 12.82% w/w. On the other hand, the MCR-ALS model had a prediction error between 1.11% w/w and 2.66% w/w, and the relative errors ranging from 0.8% to 1.74% w/w.

Technical exploration of the origins, storage periods and species identification of Boletus bainiugan

To prevent fraud in Boletus bainiugan commodities, this study provides the market with two fast and stable identification models for accurate identification of Boletus bainiugan origins, storage periods and species. Partial least squares discrimination analysis (PLS-DA), Support vector machine (SVM), Residual convolutional neural network (ResNet) and Data-driven soft independent modeling of class analogy (DD-SIMCA) models were built by combining with Fourier transform near-infrared spectroscopy (FT-NIR). The results show that the ResNet model is significant in solving the Boletus bainiugan origin identification problems. The ResNet model had the best performance and highest accuracy compared to the PLS-DA and SVM models. The DD-SIMCA model was the preferred method for the one-class classification problem, achieving an accuracy of over 96% for the Boletus bainiugan storage period and species identification. Non-target class classification accuracy reached 100%. In summary, FT-NIR combined with ResNet and DD-SIMCA models were able to solve the related identification problems of Boletus bainiugan with more satisfactory results.

Rapid Determination of Nutmeg Shell Content in Ground Nutmeg Using FT-NIR Spectroscopy and Machine Learning.

Nutmeg is a popular spice often used in ground form, which makes it highly susceptible to food fraud. Therefore, the aim of the present study was to detect adulteration of ground nutmeg with nutmeg shell via Fourier transform near-infrared (FT-NIR) spectroscopy. For this purpose, 36 authentic nutmeg samples and 10 nutmeg shell samples were analyzed pure and in mixtures with up to 50% shell content. The spectra plot as well as a principal component analysis showed a clear separation trend as a function of shell content. A support vector machine regression used for shell content prediction achieved an R2 of 0.944 in the range of 0-10%. The limit of detection of the prediction model was estimated to be 1.5% nutmeg shell. Based on random sub-sampling, the likelihood was found to be 2% that a pure nutmeg sample is predicted with a nutmeg shell content of >1%. The results confirm the suitability of FT-NIR spectroscopy for rapid detection and quantitation of the shell content in ground nutmeg.