Fourier Transform Near-infrared Spectra Research Articles

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.

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.

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.

Detection of the Early Fungal Infection of Citrus by Fourier Transform Near-Infrared Spectra

Early fungal infection of citrus is one of the common diseases found during the storage period of citrus, and fungus that infects citrus will spread to the entire batch of citrus as the degree of infection deepens, causing enormous economic losses. Therefore, early detection of fungal infection of citrus is fundamental. The purpose of this study is to explore the qualitative identification of early fungal infections in citrus by using Fourier transform near-infrared (FT-NIR) combined with a variety of chemometric methods. First, discrete wavelet transform (DWT) is used to filter the noise of the spectral signal, then combined with a PLS-DA model, that helps discriminate healthy from infected Citrus. Subsequently, four different feature variable selection methods were introduced, Then, the linear discriminant analysis (LDA) and support vector machine (SVM) two classifiers were combined to establish a qualitative model for the degree of fungal infection. The modeling results show that the SVM modeling effect is better than LDA, and the DWT-CARS-SVM based on the RBF kernel function has the best result, the accuracy rates of the training set and test set are 100% and 97%. The results indicate that FT-NIR spectroscopy, combined with chemometric methods, is able to distinguish early fungal infections in citrus.

The future of fish age estimation: deep machine learning coupled with Fourier transform near-infrared spectroscopy of otoliths

Our novel approach for fish age prediction uses quantitative analysis of Fourier transform near-infrared (FT-NIR) spectra of otoliths by means of multimodal convolutional neural networks (MMCNN). We integrate two key data modalities that are related to fish ages: the entire range of wavenumbers of FT-NIR spectra and corresponding biological and geospatial data for nearly 9000 walleye pollock ( Gadus chalcogrammus) specimens. The proposed model extracts informative spectral features automatically and elucidates hidden structural relationships associated with fish growth to improve age predictions. Absorbance associated with 7000 to 4000 cm−1 wavenumbers had the highest influence on model predictions followed by fish length, latitude, depth, and temperature. The optimal model resulted in good overall performance with an R2 of 0.93 and RMSE of 0.83 for training data set and R2 of 0.92 and RMSE of 0.91 for test data set. MMCNN's age predictions were comparable to microscope-based ages yielding as good or slightly better precision. Moreover, the model outperformed classical partial least squares analysis of otolith spectra and remedied prediction bias at older ages of fish.

Rapid and Accurate Authentication of Porcini Mushroom Species Using Fourier Transform Near-Infrared Spectra Combined with Machine Learning and Chemometrics.

Porcini mushrooms have high nutritional value and great potential, but different species are easily confused, so it is essential to identify them rapidly and precisely. The diversity of nutrients in stipe and cap will lead to differences in spectral information. In this research, Fourier transform near-infrared (FT-NIR) spectral information about imparity species of porcini mushroom stipe and cap was collected and combined into four data matrices. FT-NIR spectra of four data sets were combined with chemometric methods and machine learning for accurate evaluation and identification of different porcini mushroom species. From the results: (1) improved visualization level of t-distributed stochastic neighbor embedding (t-SNE) results after the second derivative preprocessing compared with raw spectra; (2) after using multiple pretreatment combinations to process the four data matrices, the model accuracies based on support vector machine and partial least-square discriminant analysis (PLS-DA) under the best preprocessing method were 98.73-99.04% and 98.73-99.68%, respectively; (3) by comparing the modeling results of FT-NIR spectra with different data matrices, it was found that the PLS-DA model based on low-level data fusion has the highest accuracy (99.68%), but residual neural network (ResNet) model based on the stipe, cap, and average spectral data matrix worked better (100% accuracy). The above results suggest that distinct models should be selected for dissimilar spectral data matrices of porcini mushrooms. Additionally, FT-NIR spectra have the advantages of being nondevastate and fast; this method is expected to be a promising analytical tool in food safety control.

Removal of the ambient air features from fourier-Transform near-Infrared spectra

Presence of ambient air in the optical path of Fourier-Transform single-beam near-infrared spectrometers introduces features that are external to the sample of interest and deteriorate accuracy on the time scale that is, as our data shows, similar to that of spectral acquisition. This necessitates corrective action performed either by controlling the atmosphere within the instrument or by post-processing the spectrum. Since controlling the atmosphere introduces a variety of technical issues a vast majority of spectrometers are equipped with software tools for post-processing the spectrum. A noticeable absence of applied studies on compensation of atmospheric absorption in the near-infrared spectral region prompted us to propose such a procedure from the aspect of recent advances in modeling of the ambient air absorption in this region that, in combination with a comprehensive survey of various baseline-correction algorithms, yielded a procedure for calibrated subtraction of the background single-channel spectrum that accurately models the corrupting features of the ambient air. Comparisons between different models of the total absorption of ambient air showed significant improvements that were characterized collectively, using multivariate analysis, and individually, using our novel de-trending technique, called self-subtraction. We conclude that corrections introduce measurable changes in the spectra and that implementation of this, or similar, algorithms is a prerequisite for performing quantitative analyses in the near-infrared region.

In vitro propagation of Codonopsis pilosula (Franch.) Nannf. using apical shoot segments and phytochemical assessments of the maternal and regenerated plants

BackgroundCodonopsis pilosula (Franch.) Nannf. is a medicinal plant traditionally used in China, Korea, and Japan to treat many diseases including poor gastrointestinal function, low immunity, gastric ulcers, and chronic gastritis. The increasing therapeutic and preventive use of C. pilosula has subsequently led to depletion of the natural populations of this species thus necessitating propagation of this important medicinal plant. Here, we developed an efficient and effective in vitro propagation protocol for C. pilosula using apical shoot segments. We tested various plant tissue culture media for the growth of C. pilosula and evaluated the effects of plant growth regulators on the shoot proliferation and rooting of regenerated C. pilosula plants. Furthermore, the tissues (roots and shoots) of maternal and in vitro-regenerated C. pilosula plants were subjected to Fourier-transform near-infrared (FT-NIR) spectrometry, Gas chromatography-mass spectrometry (GC–MS), and their total flavonoids, phenolics, and antioxidant capacity were determined and compared.ResultsFull-strength Murashige and Skoog (MS) medium augmented with vitamins and benzylaminopurine (1.5 mg·L−1) regenerated the highest shoot number (12 ± 0.46) per explant. MS medium augmented with indole-3-acetic acid (1.0 mg·L−1) produced the highest root number (9 ± 0.89) and maximum root length (20.88 ± 1.48 mm) from regenerated C. pilosula shoots. The survival rate of in vitro-regenerated C. pilosula plants was 94.00% after acclimatization. The maternal and in vitro-regenerated C. pilosula plant tissues showed similar FT-NIR spectra, total phenolics, total flavonoids, phytochemical composition, and antioxidant activity. Randomly amplified polymorphic DNA (RAPD) test confirmed the genetic fidelity of regenerated C. pilosula plants.ConclusionsThe proposed in vitro propagation protocol may be useful for the rapid mass multiplication and production of high quality C. pilosula as well as for germplasm preservation to ensure sustainable supply amidst the ever-increasing demand.

FT-NIR online detection performance improvement and evaluation for 2,6-dimethylphenol purity

In this paper, Fourier transform near-infrared (FT-NIR) spectroscopy was used to online detect the 2,6-dimethylphenol(2,6-DMP) purity of different rectifying columns in the distillation separation process. Considering the similarity of spectral data between different columns, transfer learning could be introduced to improve the online detection performance of 2,6-DMP purity by reusing the spectral data from other columns. Furthermore, in order to analyze the effect of the quantity and quality of the FT-NIR spectra on the detection accuracy, the influence function was used to construct the quantitative relationship between the detection accuracy and the spectral information to be transferred. The effectiveness of the approach was analyzed on the FT-NIR spectral datasets from the distillation purification process in the 2,6-DMP monomer separation section. By transferring more favorable spectral data to avoid negative transfer, the detection accuracy will be obviously increased.

Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine

Fennel contains many antioxidant and antibacterial substances, and it has very important applications in food flavoring and other fields. The kinds and contents of chemical substances in fennel vary from region to region, which can affect the taste and efficacy of the fennel and its derivatives. Therefore, it is of great significance to accurately classify the origin of the fennel. Recently, origin detection methods based on deep networks have shown promising results. However, the existing methods spend a relatively large time cost, a drawback that is fatal for large amounts of data in practical application scenarios. To overcome this limitation, we explore an origin detection method that guarantees faster detection with classification accuracy. This research is the first to use the machine learning algorithm combined with the Fourier transform-near infrared (FT-NIR) spectroscopy to realize the classification and identification of the origin of the fennel. In this experiment, we used Rubberband baseline correction on the FT-NIR spectral data of fennel (Yumen, Gansu and Turpan, Xinjiang), using principal component analysis (PCA) for data dimensionality reduction, and selecting extreme learning machine (ELM), Convolutional Neural Network (CNN), recurrent neural network (RNN), Transformer, generative adversarial networks (GAN) and back propagation neural network (BPNN) classification model of the company realizes the classification of the sample origin. The experimental results show that the classification accuracy of ELM, RNN, Transformer, GAN and BPNN models are above 96%, and the ELM model using the hardlim as the activation function has the best classification effect, with an average accuracy of 100% and a fast classification speed. The average time of 30 experiments is 0.05 s. This research shows the potential of the machine learning algorithm combined with the FT-NIR spectra in the field of food production area classification, and provides an effective means for realizing rapid detection of the food production area, so as to merchants from selling shoddy products as good ones and seeking illegal profits.

Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms.

The identification of pork meat quality is a significant issue in food safety. In this paper, a novel strategy was proposed for identifying pork meat samples at different storage times via Fourier transform near-infrared (FT-NIR) spectroscopy and fuzzy clustering algorithms. Firstly, the FT-NIR spectra of pork meat samples were collected by an Antaris II spectrometer. Secondly, after spectra preprocessing with multiplicative scatter correction (MSC), the orthogonal linear discriminant analysis (OLDA) method was applied to reduce the dimensionality of the FT-NIR spectra to obtain the discriminant information. Finally, fuzzy C-means (FCM) clustering, K-harmonic means (KHM) clustering, and Gustafson–Kessel (GK) clustering were performed to establish the recognition model and classify the feature information. The highest clustering accuracies of FCM and KHM were both 93.18%, and GK achieved a clustering accuracy of 65.90%. KHM performed the best in the FT-NIR data of pork meat considering the clustering accuracy and computation. The overall experiment results demonstrated that the combination of FT-NIR spectroscopy and fuzzy clustering algorithms is an effective method for distinguishing pork meat storage times and has great application potential in quality evaluation of other kinds of meat.

Spectral Range and Wood Surface Impacts on Equilibrium Moisture Content Estimation in Thermally Modified Beech Wood by FT-NIR Spectroscopy

Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares regression (PLS-R) were tested for the possibility of equilibrium moisture content (EMC) prediction in thermally modified beech wood (Fagus moesiaca C.). The samples were modified for 4h at temperatures of 170, 190 and 210 °C. After thermal modification, the samples were kept in a climatic chamber until EMC was reached. FT-NIR spectra (100 scans and 4 cm-1) were collected on the cross-section and radial surfaces at four points. PLS – R models were developed for four spectral regions: the first overtone, the second overtone, the third overtone and the combination band region. Applied thermal treatment caused a decrease of EMC by 42 % at 170 °C, by 53 % at 190 °C, and by 62 % at 210 °C. Principal component analysis (PCA) indicated that there is a difference both between treatments and between wood surfaces. The results of the spectra taken from the radial surface were, in all models, better than the spectra of the cross-section. Related to chemical changes, the first and second overtone region play an important role in the calibrations. The best prediction models for EMC of thermally modified beech wood were obtained from radial surface spectra in the first (Rp2=0.86, RPD=2.69) and second overtone region (Rp2=0.87, RPD=2.70). The obtain results could contribute to the development of predictive models in monitoring of EMC which could significantly improve the quality of industrial production of thermally modified wood.

Evaluation of Chemical Quality on Juices and Wine Produced from Mamao Fruit (Antidesma Puncticulatum Miq.) Within Near-Infrared Spectroscopy

The chemical quality of juices and wine produced from Mamao fruit was evaluated by Fourier transform near-infrared (FT-NIR). The calibration equation was created by the cross-validation method to be si+mulated the accuracy. Statistical values composed of correlation coefficient (R), standard error of cross-validation (SECV) and bias were used. Brix values and acidity values of Mao juice and the Brix, acidity, and alcohol values of Mao wine products were evaluated through the standard and cross-validation relation. It was found that was observed with NIR spectrometer to be absorbed in the same IR wavelength (1450 nm) which indicated that the water is the main composition. Based on FT-NIR analysis, the spectrum latices of juices and wine were revealed in the same range of the absorption bands at 1450 nm and 1940 nm to be confirmed the water composition. Also, the FT-NIR spectra from region 2258-2312 nm in Mao wine product have been predicted to the Ethanol functions.

Rapid age estimation of longnose skate (

There is a paucity of age data for chondrichthyan fishes owing, in large part, to limitations in traditional age estimation methods. Fourier transform near-infrared (FT-NIR) spectroscopy has shown promise as an alternative, more efficient method for acquiring age data from chondrichthyans. However, studies are limited to sharks in the southern hemisphere. We explored FT-NIR spectroscopy to predict age for a batoid species in the northern hemisphere. The longnose skate (Raja rhina) is one of a small number of batoids for which annual band periodicity in vertebral centra has been validated, allowing for traditional age estimation and making it an ideal candidate for this study. We fit a multivariate partial least-square predictive model between FT-NIR spectra collected from vertebral centra and traditional age estimates, and tested model predictive skill by using external validation. Using FT-NIR spectroscopy, we were able to predict age for longnose skates between the ages of 1 and 14 years with precision and bias near equal to those of traditional methods in less than a quarter of the time. These results support potential for FT-NIR spectroscopy to increase the amount of age data available for assessments used to inform the conservation and management of this sensitive group of species.

An in vitro Propagation of Aspilia africana (Pers.) C. D. Adams, and Evaluation of Its Anatomy and Physiology of Acclimatized Plants.

Aspilia africana (Pers.) C. D. Adams is an important medicinal plant, that has been used as traditional medicine in many African countries for the treatment of various health problems, including inflammatory conditions, osteoporosis, tuberculosis, cough, measles, diabetes, diarrhea, malaria, and wounds. We developed an efficient and reproducible protocol for in vitro regeneration of A. africana from nodes. We assessed the effects of plant tissue culture media on A. africana growth, cytokinins for in vitro shoot regeneration and proliferation, and auxins for the rooting of regenerated shoots. Furthermore, chlorophyll content, photosynthetic rates, anatomy (leaves, stems, and roots), and Fourier transform near-infrared (FT-NIR) spectra (leaves, stems, and roots) of the in vitro regenerated and maternal A. africana plants were compared. Murashige and Skoog media, containing vitamins fortified with benzylaminopurine (BA, 1.0 mg/l), regenerated the highest number of shoots (13.0 ± 0.424) from A. africana nodal segments. 1-naphthaleneacetic acid (NAA, 0.1 mg/l) produced up to 13.10 ± 0.873 roots, 136.35 ± 4.316 mm length, and was the most efficient for rooting. During acclimatization, the in vitro regenerated A. africana plants had a survival rate of 95.7%, displaying normal morphology and growth features. In vitro regenerated and mother A. africana plants had similar chlorophyll contents, photosynthetic rates, stem and root anatomies, and FT-NIR spectra of the leaf, stem, and roots. The established regeneration protocol could be used for large-scale multiplication of the plant within a short time, thus substantially contributing to its rapid propagation and germplasm preservation, in addition to providing a basis for the domestication of this useful, high-value medicinal plant.

Prediction of olive ripening degree combining image analysis and FT-NIR spectroscopy for virgin olive oil optimisation

This work proposes classification models for the prediction of olive maturity index based on Fourier Transform-Near Infrared (FT-NIR) spectra of intact drupes. An image analysis (IA) method was purposely developed for the objective evaluation of the maturity index. Thirteen cultivars at different ripening stages were harvested along three years. The reliability of the IA method was confirmed by the highly significant correlation with the common visual evaluation of maturity index. Classification models were developed with Partial Least Square-Discriminant Analysis (PLS-DA), using IA results and FT-NIR spectra of olives collected in diffuse reflectance. Most PLS-DA models calculated separately for olive origin gave sensitivity and specificity values in prediction higher than 81%. The global model performed slightly worse (sensitivity, 79%; specificity, 75%), but it is definitely more robust and can provide the olive sector with a fast, green and non-destructive olive sorting method for the production of high quality virgin oil.

Nondestructive measurement of anthocyanin in intact soybean seed using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) spectroscopy

The content of phytochemical compounds such as anthocyanin in soybeans, yielded from agricultural practices is usually affected by the quality of the mother seed. Therefore, non-destructive technique for intact soybean seed selection based on anthocyanin content is highly demanded for breeding programs. The objective of this study was to evaluate the feasibility of Fourier-transform near-infrared (FT-NIR) and Fourier-transform infrared (FT-IR) spectroscopic techniques to determine the total anthocyanin content (TAC) and content of different types of anthocyanin, namely cyanidin-3-glucoside (C3G) and delphinidin-3-glucoside (D3G), in single seed state of soybean. FT-NIR and FT-IR spectra from 70 different varieties of soybean seeds were acquired and compared with the chemical components analyzed using high-performance liquid chromatography (HPLC). The prediction performance of the partial least squares regression (PLSR) models for the FT-NIR spectra indicated R2 of 0.88–0.90 and standard error of prediction (SEP) of 9.4–19.5% for the chemical components, which were slightly better than R2 of 0.86–0.88 and SEP of 9.7–21.8% of FT-IR. The number of variables could be reduced down to 50% using a variable importance in projection (VIP) method without noticeable decrements in prediction performance. The results demonstrated potential of FT-NIR and FT-IR spectroscopic techniques to predict anthocyanin contents in a single seed of soybean nondestructively.

FT-NIR coupled chemometric methods rapid prediction of K-value in fish

A technique for predicting the K-value of fish freshness using Fourier Transform-Near-Infrared (FT-NIR) coupled with chemometric algorithms was developed. FT-NIR spectra of 150 fresh fish were acquired, and their respective K-value estimated using HPLC procedures. Acquired spectra were pretreated using standard normal variate (SNV) to eliminate extraneous information. The developed FT-NIR coupled chemometric algorithm attained K-values of the correlation coefficient between 0.9471–0.9786 and the predictive deviation of the residuals (RPD) of 3.53–4.19. The performance of the ant colony partial least squares (ACO-PLS) compared to the other chemometric algorithms proved superior for the estimation of K-values of fish freshness with a correlation coefficient of 98.27 % for the training set and 97.86 % for the test set recorded. This implies that FT-NIR, coupled with different chemometric algorithms, has the potential to be deployed for accurate prediction of K-value as a freshness indicator.

Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry.

The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.

Forensic classification of black inkjet prints using Fourier transform near-infrared spectroscopy and Linear Discriminant Analysis

This work presents a study regarding the forensic discrimination of black inkjet-printed documents in question. Nondestructive Fourier transform near-infrared (FT-NIR) spectroscopy in combination with supervised classification method Discriminant Analysis (DA); Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) were utilized to investigate 22 different prints of the three most sold office printer brands. The spectra were acquired using the FT-NIR spectrometer NIRFlex N-500 (Büchi Labortechnik AG, Flawil, Switzerland) with the Fiber Optic Solids measuring cell in the spectral region of 10,000–4000 cm−1. Each sample was printed on the same type of office paper. The spectra were 45 times acquired on 3 separate printed squares of each sample. It results in 990 acquired spectra for the presented experiment. The FT-NIR spectra of the printed squares were split into calibration and test sets with which the Classification accuracy (CA) value of unknown samples was evaluated. In order to increase the significance, three different compilations of calibration and test sets were realized. The performance of three different Discriminant model methods; LDA (Euclidean and Mahalanobis algorithm) and QDA were compared to each other. Furthermore, the CA of each DA method was examined using 1–5 principal components (PCs) in the construction of the respective DA model. Two groups of models, according to the ink subset (Carbon black and Black colorant), were performed in raw and Standard Normal Variate (SNV) correction spectra alternations. The results showed that the Euclidean method yielded the highest accuracy in predicting independent test samples and thus clearly outperformed the QDA and Mahalanobis algorithm DA method. It was also determined that the ink type Carbon black had higher CA values than the ink type Black colorant. This work demonstrated the special ability of FT-NIR spectroscopy in combination with DA to examine inkjet-printed documents in a fast and non-destructive fashion.