Fourier Transform Near-infrared Spectroscopy Research Articles

Rapid determination of fat content: Advanced spectroscopic methods across diverse fish species

The rapid estimation of fat content using spectroscopic-based sensors, irrespective of fish species (salmon, trout, sea bass, sea bream, tuna, cod, and mackerel), was primarily investigated in the present study. The fat content and fatty acid (FA) composition was quantified with reference methods. Fourier-Transform Infrared (FTIR) and Fourier-Transform Near-Infrared (FT-NIR) spectroscopy, along with multispectral imaging (MSI) instruments (both benchtop and portable), were evaluated for their ability to rapidly predict fat content in ground samples using data analysis. The performance of the PLS-R models was evaluated according to root mean square error (RMSE), coefficient of determination (R2) and residual prediction deviation (RPD). Also fish were classified as fat/low-fat and low/medium/high-fat using partial least squares discriminant analysis (PLS-DA). All fish species exhibited a consistent pattern of unsaturated fatty acids (UNFA) > monounsaturated fatty acids (MUFA) > polyunsaturated fatty acids (PUFA) > saturated fatty acids (SFA) and the range of fat content was from 22.8 (salmon) to 0.02 (cod), expressed in g/100 g of sample based on the reference methods. In terms of fat content prediction using rapid sensors the best performance indices of the test set were obtained from the benchtop-MSI (RMSE = 1.475, R2 = 0.847, RPD = 2.581) and FT-NIR (RMSE = 1.638, R2 = 0.855, RPD = 2.651) instruments, while FTIR and portable-MSI had scores of RMSE = 1.874, R2 = 0.815, RPD = 2.309 and RMSE = 1.737, R2 = 0.786, RPD = 2.191, respectively. All sensors discriminated fat from low-fat samples (accuracy = 100%). For low/medium/high fat the best results were achieved by benchtop-MSI (accuracy = 94.12%), followed by portable-MSI (accuracy = 88.24%) and FTIR (accuracy = 84.21%). Results were less satisfactory for the FT-NIR method (accuracy = 68.75%). This study demonstrates that vibrational and multispectral imaging spectroscopies, when coupled with data analysis, have potential to provide information about the nutritional quality (e.g., fat content) independent of the fish species. Such insights could contribute to the automation and potential digitalization of processes within the fields of food nutrition and the seafood industry.

Rapid discrimination and quantification of chemotypes in Perillae folium using FT-NIR spectroscopy and GC–MS combined with chemometrics

Perillae Folium (PF) is a well-known food and herb containing different chemotypes, which affect its quality. Herein, a method was proposed to classify and quantify PF chemotypes using gas chromatography–mass spectrometry (GC–MS) and Fourier transform-near infrared spectroscopy (FT-NIR). GC–MS results revealed that PF contains several chemotypes, including perilla ketone (PK) type, α-asarone (PP-as) type, and dillapiole (PP-dm) type, with the PK type being the predominant chemotype. Based on FT-NIR data, different chemotypes were accurately classified. The random forest algorithm achieved >90 % accuracy in chemotype classification. Furthermore, the main components of perilla ketone and isoegomaketone in PF were successfully quantified using partial least squares regression models, with prediction to deviation values of 3.76 and 2.59, respectively. This method provides valuable insights and references for the quality supervision of PF and other foods.

Characterization of volatile profiles and markers prediction of eleven popular edible boletes using SDE-GC–MS and FT-NIR combined with chemometric analysis

Wild edible boletes mushrooms are regarded as a delicacy in many countries and regions due to their rich nutritional contents and strong aromatic compounds. This study aimed to identify 445 samples of 11 boletes species collected from Yunnan and Sichuan provinces through molecular analysis. Using simultaneous distillation–extraction (SDE) combined with gas chromatography-mass spectrometry (GC–MS), 97 volatile compounds were identified. Chemometric methods were then applied to analyze the heterogeneity of these volatile compounds among the different species. The results showed that, 22 and 21 volatile compounds were selected using variable importance in projection (VIP > 1) and relative odor activity values (ROAV > 0.1), respectively. Partial least squares discrimnatint analysis (PLS-DA) was then employed to develop pattern recognition models for 11 species, which demonstrated strong identification performance. Furthermore, correlation heat maps, volcano plots, and Fisher linear discriminant analysis identified five volatile organic compounds, including methyl (9E)-9-octadecenoate, 2, 6-dimethylpyrazine, 1-decen-3-one, furfural, and methional as markers for distinguishing 11 boletes species. Ultimately, the rapid content prediction models of partial least squares regression (PLSR) were established by combining Fourier Transform Near-Infrared Spectroscopy (FT-NIR) with the concentrations of these five marker compounds. These findings provide a methodological strategy for the effective species identification of wild edible mushrooms and the rapid prediction of their characteristic aroma compounds.

Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT-NIR spectroscopy: Quantification and classification insights.

Cannabis sativa L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time-consuming. This study explores the use of Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT-NIR spectroscopy on wet versus dry cannabis inflorescences. Spectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares-discriminant analysis (PLS-DA) and partial least squares-regression (PLS-R) models. The PLS-DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS-R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT-NIR spectra for the first time, achieving cross-validation and prediction R-squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low-cannabidiolic acid submodel and (-)-Δ9-trans-tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence. These findings suggest that FT-NIR spectroscopy can be a viable rapid on-site analytical tool for growers during the inflorescence flowering stage.

Effect of Light Irradiance and Curing Duration on Degree of Conversion of Dual-Cure Resin Core in Various Cavities with Different Depths and Diameters.

(1) Background: To compare the degree of conversion of resin cores in various types of cavities and determine an effective irradiation method for achieving a higher degree of conversion. (2) Methods: Four different-sized cavities (narrow-shallow, narrow-deep, wide-shallow, and wide-deep) were simulated using a Teflon mold. The light irradiance reaching the bottom of each mold was measured by positioning a radiometer. The degree of conversion of the dual-cure resin core after irradiation (400 mW/cm2 for 20 s, 400 mW/cm2 for 40 s, and 800 mW/cm2 for 20 s) was measured using Fourier-transform near-infrared spectroscopy. (3) Results: The highest light irradiance was found at the bottom of wide-shallow cavities, followed by wide-deep, narrow-shallow, and narrow-deep ones (p < 0.001). In narrow cavities, irradiation at 800 mW/cm2 for 20 s led to a significantly higher degree of conversion (p < 0.001). In wide cavities, irradiation at 400 mW/cm2 for 40 s and 800 mW/cm2 for 20 s both led to a significantly higher degree of conversion (p < 0.001). (4) Conclusions: Less curing light reaches the bottom of cavities with a smaller diameter and greater depth. Providing a higher irradiance of light can induce a higher degree of conversion of resin composites in narrower cavities.

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

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

Fourier-transform near-infrared spectroscopy first application to age determination in European fish species: the case of the Atlantic horse mackerel from the central Mediterranean Sea

Fourier-transform near-infrared spectroscopy (FT-NIRS) has recently been used to reduce the processing time for estimates of annual age in several fish species. The present study represents the first application of this technique to marine organisms from the European waters. Atlantic horse mackerel (Trachurus trachurus) from the central Mediterranean Sea was selected for its ecological role, its socioeconomic value, and because its age is regularly estimated by otolith reading under a stereomicroscope for stock assessment purposes. Absorption spectra of the whole otoliths were acquired by FT-NIRS across a multiyear dataset, obtained during acoustic surveys carried out in different regions of the central Mediterranean Sea. The acquired spectra were processed to optimize calibration models to predict age. The best linear models obtained by the optimizing procedure predicted age successfully with a coefficient of determination of 0.95–0.96, mean squared error of 0.5 years, and bias &amp;lt;0.03 years. Although regional calibration models were also obtained separately, the models with all regions combined appeared much more robust and encompassing the whole age range, suggesting its usefulness for the entire spatial distribution range considered in the preset study. The use of FT-NIRS allowed to successfully predict age for horse mackerel between the ages 1 and 14, with similar or better precision (and bias) of the traditional methods, also increasing the standardization by repeatability of age assignments. The preliminary results obtained by this study encourage further effort to fine-tune the calibration model for new species, in order to apply a precise, rapid, cost-effective method for stock assessment purposes of Mediterranean fish resources.

High-precision detection of dibutyl hydroxytoluene in edible oil via convolutional autoencoder compressed Fourier-transform near-infrared spectroscopy

The quality of edible oils is closely related to their chemical compositions. Antioxidants have widespread application in edible oil production. In this study, a pioneering detection approach involving the use of a one-dimensional convolutional autoencoder (1D-CAE) was introduced to compress spectral data for assessing antioxidant levels in edible oils. Fourier-transform near-infrared (FT-NIR) characterisation of edible oil samples with varying antioxidant concentrations was also conducted. An 1D-CAE model was developed to compress different pre-processed spectra into a condensed representation. These compressed features were then integrated with a support vector machine and partial least squares regression models to establish correlations for each target. The study examined the influence of pre-processing steps and feature engineering methods on near-infrared spectral analysis through independent or combined model analysis. The findings revealed that features derived from the 1D-CAE model demonstrated remarkable repeatability and can be utilised to construct robust detection models. The experimental results showed that the optimal detection model derived based on the 1D-CAE compression features has an average R2, RPD and RMSE of 0.9953, 15.1664 and 1.2035, respectively, on the prediction set. FT-NIR spectroscopy can be used to accurately detect butylated hydroxytoluene in edible oils. Therefore, autoencoders are an effective tool in spectroscopic analysis, offering promising avenues for future research and application.

Geographical origin determination of the PDO hazelnut (cv. Giresun Tombul) by chemometric analysis of FT-NIR and Raman spectra acquired from shell and kernel.

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes

The taste and aroma of edible mushrooms, which is a criterion of judgment for consumer purchases, are influenced by amino acids and their metabolites. Sixty-eight amino acids and their metabolites were identified using liquid chromatography mass spectrometry (LC-MS), and 16 critical marker components were screened. The chemical composition of different species of boletes was characterized by two-dimensional correlation spectroscopy (2DCOS) to determine the sequence of molecular vibrations or group changes. Identification of boletes species based on partial least squares discrimination (PLS-DA) combined with Fourier transform near-infrared spectroscopy (FT-NIR) and Fourier transform infrared spectroscopy (ATR-FTIR), residual convolutional neural network (ResNet) combined with three-dimensional correlation spectroscopy (3DCOS) was performed with 100% accuracy. Partial least squares regression (PLSR) analysis showed that FT-NIR and ATR-FTIR spectra were highly correlated with the amino acids and their metabolites detected by LC-MS. All models had achieved an R2p of 0.911 and an RPD >3.0. The results show that FT-NIR and ATR-FTIR spectroscopy in combination with chemometrics methods can be used for rapid species identification and estimation of amino acids and their metabolites content in boletes. This study provides new techniques and ideas for the authenticity of species information and the quality assessment of boletes.

The use of chemometrics in combination with molecular spectroscopy and chromatography methods for determining the levels of gingerol compounds in ginger (Zingiber officinale): a review

Ginger (Zingiber officinale Roscoe), a member of the Zingiberaceae family, is a herb with a global reputation in medicines, food seasonings, beverages and cosmetics. It is rich in antioxidants due to some active components, including gingerol, shogaol and zingerone. These components are reported to have more powerful antioxidants than vitamin E. The phytochemicals and their levels are significantly influenced by environmental factors such as harvesting time, soil condition, and the cultivating place. Therefore, this study highlighted the analytical method to determine the gingerol content in ginger using chemometrics. Several databases including Scopus, PubMed, and Science Direct, were explored to obtain relevant articles using specific keywords related to the reviewed topic. Several chemometrics methods are used for the characterization and profiling of fingerprints, such as Fourier-transform infrared spectroscopy (FTIR), Fourier-transform near infrared spectroscopy (FT-NIR), high-performance liquid chromatography (HPLC), and High-performance thin-layer chromatography (HPTLC), including pattern recognition and multivariate calibration combined with molecular spectroscopy and chromatography. The fingerprint profiling is processed and combined with chemometrics analysis or multivariate data for faster, sensitive and valid results.

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 &lt; 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 &gt; 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 &lt; 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) &gt; 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.