FT-NIR Spectra Research Articles

Portable Raman spectroscopy and fourier transform near infrared spectroscopy for the quantification of different sinomenine hydrochloride crystal forms

The objective of this paper is to rapidly and accurately quantify the content of the dominant crystal form of Sinomenine hydrochloride (SH) and to evaluate the respective characteristics of Raman spectroscopy and Fourier transform near infrared spectroscopy techniques for rapid quantification of crystalline substances. In this study, we performed an adulterated gradient quantification based on two new crystalline forms of SH prepared in the laboratory in combination with commercially available products. And established 86 samples containing 66 batches of ternary and 20 batches of binary mixtures. We quantified SH possessing polycrystalline forms based on two kinds of spectroscopy techniques combined with chemometric methods. In the process of establishing the partial least squares quantitative model, according to the experimental design thought, we used the ensemble preprocessing method to screen the optimal preprocessing method for the spectral data of crystal samples. Then we optimized four variable selection methods by single factor investigation. Finally, after completing the PLSR model, we found that the regression models established for both Raman spectra (RMSEP = 0.00672) and FT-NIR spectra (RMSEP = 0.00533) have a high ability to rapidly quantify the crystals.

Strategies for the quality control of Chrysanthemi Flos: Rapid quantification and end-to-end fingerprint conversion based on FT-NIR spectroscopy.

Chrysanthemi Flos (CF) is widely used as a natural medicine or tea. Due to its diverse cultivation regions, CF exhibits varying quality. Therefore, the quality and swiftness in evaluation holds paramount significance for CF. The aim of the study was to construct a comprehensive evaluation strategy for assessing CF quality using HPLC, near-infrared (NIR) spectroscopy, and chemometrics, which included the rapid quantification analyses of chemical components and the Fourier transform (FT)-NIR to HPLC conversion of fingerprints. A total of 145 CF samples were utilised for data collection via NIR spectroscopy and HPLC. The partial least squares regression (PLSR) models were optimised using various spectral preprocessing and variable selection methods to predict the chemical composition content in CF. Both direct standardisation (DS) and PLSR algorithms were employed to establish the fingerprint conversion model from the FT-NIR spectrum to HPLC, and the model's performance was assessed through similarity and cluster analysis. The optimised PLSR quantitative models can effectively predict the content of eight chemical components in CF. Both DS and PLSR algorithms achieve the calibration conversion of CF fingerprints from FT-NIR to HPLC, and the predicted and measured HPLC fingerprints are highly similar. Notably, the best model relies on CF powder FT-NIR spectra and DS algorithm [root mean square error of prediction (RMSEP) = 2.7590, R2 = 0.8558]. A high average similarity (0.9184) prevails between predicted and measured fingerprints of test set samples, and the results of the clustering analysis exhibit a high level of consistency. This comprehensive strategy provides a novel and dependable approach for the rapid quality evaluation of CF.

Data fusion of FT-NIR and ATR-FTIR spectra for accurate authentication of geographical indications for Gastrodia elata Blume

Gastrodia elata Blume (G. elata Bl.), with its excellent nutritional and medicinal value from Zhaotong, has been protected by geographical indication (GI). Accurate certification of its origin is a prerequisite to safeguard consumer interests and maintain the market. Four different regions and three varieties of G. elata Bl. from Zhaotong were used in this study (n = 262). Tri-step infrared spectroscopy was used for ATR-FTIR spectral analysis to filter out fingerprint regions for data fusion with FT-NIR spectra, after which conventional discriminant models (PLS-DA and GS-SVM) were built. The second derivative (SD), multiple scattering correction (MSC), and Savitzky-Golay (SG) preprocessing were also performed on the spectra, and it was found that the preprocessing improved the performance of the PLS-DA model. The optimal model results in GS-SVM, based on mid-level data fusion of principal components (PCs) and latent variables (LVs), with sensitivity, specificity, accuracy, and Matthews correlation coefficient (MCC) of 1 for the test set. Furthermore, the residual convolutional neural network (ResNet) models were built, based on FT-NIR full spectra, band 3600−2700 cm−1 (MFA) and band 1750−500 cm−1 (MFB). Their accuracy in both train and test sets exceeds 97%, and the loss function curve is close to 0, which indicates that these three bands can be used as a fingerprint area to verify the GI of Zhaotong G. elata Bl. This study provides a fast, non-invasive method for the authentication of food or medicinal plant GI.

Phase shift cavity ring down and FT-NIR spectra of C[sbnd]H vibrational transitions and band strengths of ethyl acetate

The vibrational overtone spectra (Δυ = 5, 6) of CH transitions of ethyl acetate (CH3COO–CH2CH3) have been obtained using the phase shift cavity ring down (PS-CRD) technique at 295 K. The CH stretch fundamental and overtone transitions (Δυ = 1- 4) have been obtained using a Fourier transform infrared and near infrared spectrophotometer. Local mode frequencies ωe and anharmonicities ωexe are reported for the CH (ip) (in plane), CH (op) (out of plane) bonds of the methyl group attached to the carbonyl (CO) side and for the methyl CH3 (primary) and methylene CH2 (secondary) CH bonds of the ethyl group bonded to the O atom. The study includes computer deconvolution of the total band at each level and a density functional theory (DFT) calculation of deuterated analogues of ethyl acetate to help with the assignment of all the non-equivalent CH bonds in the molecule. From the assigned Δυ = 6 peak wavenumber, isolated frequencies and CH bond lengths are obtained for the different CH bonds. Band strengths, oscillator strengths, and transition moments were obtained experimentally for all transitions. The importance of the assigned vibrational transitions and integrated intensities for future studies of laser and atmospheric vibrational overtone photochemistry is discussed.

Quality Monitoring of Biodiesel and Diesel/Biodiesel Blends: A Comparison between Benchtop FT-NIR versus a Portable Miniaturized NIR Spectroscopic Analysis

A methodology such as near-infrared (NIR) spectroscopy, which enables in situ and in real-time analysis, is crucial to perform quality control of biodiesel, since it is blended into diesel fuel and the presence of contaminants can hinder its performance. This work aimed to compare the performance of a benchtop Fourier Transform (FT) NIR spectrometer with a prototype of a portable, miniaturized near-infrared spectrometer (miniNIR) to detect and quantify contaminants in biodiesel and biodiesel in diesel. In general, good models based on principal component analysis-linear discriminant analysis (PCA-LDA) of FT-NIR spectra were obtained, predicting with high accuracies biodiesel contaminants and biodiesel in diesel (between 75% to 95%), as well as good partial least square (PLS) regression models to predict contaminants concentration in biodiesel and biodiesel concentration in diesel/biodiesel blends, with high coefficients of determination (between 0.83 and 0.99) and low prediction errors. The miniNIR prototype’s PCA-LDA models enabled the prediction of target contaminants with good accuracies (between 66% and 86%), and a PLS model enabled the prediction of biodiesel concentration in diesel with a reasonable coefficient of determination (0.68), pointing to the device’s potential for preliminary analysis of biodiesel which, associated with its potential low cost and portability, could increase biodiesel quality control.

Near infrared spectroscopy and seedling image analysis to evaluate the physiological potential of Urochloa decumbens (Stapf) R.D. Webster seeds

Abstract: The demand for techniques that make it possible to assess the physiological potential of seeds quickly and assertively make near-infrared spectroscopy (FT-NIR) and seedling analysis using ILASTIK software promising tools. The aim of this study was to evaluate the physiological potential of Urochloa decumbens seeds using near-infrared spectroscopy (FT-NIR) and ILASTIK software. Seeds from 10 lots of U. decumbens were classified according to their physiological potential (germination and vigor) and then FT-NIR spectra were obtained from individual seed samples, the original spectra being pre-processed with different dispersion correction methods used for the construction of a classification model through partial least squares discriminant analysis (PSL-DA). For the ILASTIK evaluation, the seedlings were photographed at 7 and 14 days of germination and the trained classifier was applied to the images, generating data on the numbers of strong seedlings, weak seedlings and non-germinated seeds. With data from the FT-NIR technique pre-processed by the 2nd derivative of Savitzky-Golay, it was possible to obtain a classification model with high efficiency to discriminate the classes regarding the physiological potential of the seeds. ILASTIK was efficient to classify seeds according to their physiological potential after only 7 days of germination. FT-NIR and ILASTIK analyses are non-destructive and fast alternatives, with great potential for quality control of U. decumbens seed lots.

Free Phenolic Compounds, Antioxidant Capacity and FT-NIR Survey of Debittered Lupinus mutabilis Seeds

Lupinus mutabilis protein-rich seeds must be debittered before consumption. The aim of this research was to assess free phenolic compounds, antioxidant capacity and FT-NIR spectra of flours from debittered seeds of 33 Andean ecotypes of L. mutabilis, and five varieties belonging to L. luteus, L. angustifolius and L. albus, as controls. The free phenolics were quantified by RP-HPLC, while the antioxidant capacity was evaluated spectrophotometrically through the Reducing Power, ABTS, FRAP and DPPH methods. The free phenolics of L. mutabilis were mostly (85.5–99.6%) flavonoids (genistein and genistein derivatives, apigenin, catechin and naringenin). Other compounds, detected in low quantities, were phenylethanoids (tyrosol and tyrosol derivative) and phenolic acids (cinnamic acid derivatives). The highest total free phenolic concentration was observed in H6 INIA BP (1393.32 mg/kg DM), followed by Chacas, Moteado beige, Huánuco and Lircay. The antioxidant capacity of the L. mutabilis ecotypes exceeded that of the controls and was correlated to flavonoids content. Additionally, a relationship between free phenolic compounds and spectral bands was established by FT-NIR, paving the way for a fast, reliable and non-destructive approach to lupin seeds characterisation. Even after debittering, lupin flours maintained high free phenolic concentrations and antioxidant capacity.

A Variable Selection Method Based on Fast Nondominated Sorting Genetic Algorithm for Qualitative Discrimination of Near Infrared Spectroscopy

A reliable and effective qualitative near-infrared (NIR) spectroscopy discrimination method is critical for excellent model building, yet the performance of models built by these methods is highly dependent on valid feature extraction. The goal of feature selection is to associate the selected variables with the property of interest, which many have done successfully. However, many of selection methods focus only on strong association with the analytes or properties of interest, neglecting correlations between variables. A variable selection method based on a fast nondominated-ranking genetic algorithm (NSGA-II) was proposed in this paper for qualitative discrimination of NIR spectra. The method had two objective functions: (1) maximizing the sum of ratios of interclass variance to intraclass variance, (2) minimizing the sum of correlation coefficients between the selected variables. FT-NIR spectra of a total of 124 tobacco samples from different origins and parts in Guizhou Province, China, were used as the experimental objects, and the part-grade discrimination models of tobacco leaves were established by combining this method with partial least squares-based discriminant analysis (PLS-DA), and compared with PLS-DA model based on the full spectrum. The results showed that the performance of PLS-DA model with the NSGA-II was improved, with a comparable or better correct discrimination rate and reasonable discrimination rate, and could discriminate different parts of the tobacco leaves well. It indicates that the NSGA-II can select a few and effective feature variables to build a high-performance qualitative discrimination model and is proved to be a promising algorithm. In addition, the method is not designed exclusively for spectral data. It is a general strategy that could be used for variable selection for other types of data.

Identification of Thai coconut sugar (Cocos nucifera L.) and prediction moisture content in Thai coconut sugar using FT-NIR spectroscopy

Coconut sugar is a local sugar from the blossoms of a coconut tree. It has been considered a healthy sugar due to its low glycemic index. There is an attempt to add other sugar to it to lower the cost. Thus, this research aimed to identify Thai coconut sugar and to establish models for predicting the moisture content of coconut sugar by using FT-NIR spectroscopy. Thai coconut sugar samples were purchased from local grocery stores in four provinces, online, and the community market. Their moisture contents were varied and equilibrated for 24 hrs prior to the measurements of moisture and FT-NIR spectra. The results showed that FT-NIR spectra of Thai coconut sugar differ from sucrose, glucose and fructose at the absorbance spectrum of 5379-5011 cm-1 . FT-NIR spectroscopy of 54 known moisture samples of Thai coconut sugar was used to obtain a model to predict moisture content. The predicted equation, using the PLS technique with the Spectrum Quant program, was found to give a standard error of prediction (SEP) 0.077% (less than 0.10%), indicating a non-destructive method of accurately and precisely predicting moisture levels in the coconut sugar. The results obtained suggested that FTNIR spectroscopy has the potential to be used as a tool to identify Thai coconut sugar accurately. It can rapidly predict the moisture content in the sample which will be useful in quality control standards.

Spectroscopic Discrimination of Bee Pollen by Composition, Color, and Botanical Origin.

Bee pollen samples were discriminated using vibrational spectroscopic methods by connecting with botanical sources, composition, and color. SEM and light microscope images of bee pollen loads were obtained and used to assess the botanical origin. Fourier transform (FT) mid- and near-infrared (FT-MIR, FT-NIR), and FT-Raman spectra of bee pollen samples (a set of randomly chosen loads can be defined as an independent sample) were measured and processed by principal component analysis (PCA). The CIE L*a*b* color space parameters were calculated from the image analysis. FT-MIR, FT-NIR, and FT-Raman spectra showed marked sensitivity to bee pollen composition. In addition, FT-Raman spectra indicated plant pigments as chemical markers of botanical origin. Furthermore, the fractionation of bee pollen was also performed, and composition of the fractions was characterized as well. The combination of imaging, spectroscopic, and statistical methods is a potent tool for bee pollen discrimination and thus may evaluate the quality and composition of this bee-keeping product.

Evaluation of physico-chemical changes and FT-NIR spectra in fresh egg pasta packed in modified atmosphere during storage at different temperatures

Physico-chemical properties (i.e. water activity, colour, water absorption index and hardness) influence stability, quality and consumer acceptability of fresh pasta. The aim of this study was to investigate the effect of storage time on physico-chemical properties of fresh pasta stored at different temperatures (0, 5 and 10 °C). Storage time significantly affected the macroscopic properties (water absorption index and hardness) of fresh pasta and its water status (water activity, freezable water). During storage at all temperatures, water activity and freezable water decreased, confirming a reduction in water mobility in the product. Storage time increased pasta hardness and caused a reduction in the water absorption index. Fresh egg pasta retained its yellow colour, while lipid oxidation indices were only slightly affected. With the increase of storage temperature, the kinetic rate of water activity and gelatinisation degree decreased. In contrast, the kinetic rate of pasta hardness increased with the temperature. The Arrhenius relationship explained the temperature dependence of reaction constants, while FT-NIR analysis showed a structural reorganisation of pasta matrix during storage. Generally, pasta matrix reorganisation involved water, starch and proteins interactions, and storage temperature was an important factor affecting these modifications with specific effects on physico-chemical pasta properties (e.g. hardness).

Classification of polymorphic forms of fluconazole in pharmaceuticals by FT-IR and FT-NIR spectroscopy

The main goal of this work was to test the ability of vibrational spectroscopy techniques to differentiate between different polymorphic forms of fluconazole in pharmaceutical products. These are mostly manufactured with fluconazole as polymorphic form II and form III. These crystalline forms may undergo polymorphic transition during the manufacturing process or storage conditions. Therefore, it is important to have a method to monitor these changes to ensure the stability and efficacy of the drug.Each of FT-IR or FT-NIR spectra were associated to partial least squares-discriminant analysis (PLS-DA) for building classification models to distinguish between form II, form III and monohydrate form. The results has shown that combining either FT-IR or FT-NIR to PLS-DA has a high efficiency to classify various fluconazole polymorphs, with a high sensitivity and specificity. Finally, the selectivity of the PLS-DA models was tested by analyzing separately each of three following samples by FT-IR and FT-NIR: lactose monohydrate, which is an excipient mostly used for manufacturing fluconazole pharmaceutical products, itraconazole and miconazole. These two last compounds mimic potential contaminants and belong to the same class as fluconazole. Based on the plots of Hotelling’s T² vs Q residuals, pure compounds of miconazole and itraconazole, that were analyzed separately, were significantly considered outliers and rejected. Furthermore, binary mixtures consist of fluconazole form-II and monohydrate form with different ratios were used to test the suitability of each technique FT-IR and FT-NIR with PLS-DA to detect minimum contaminant or polymorphic conversion from a polymorphic form to another using also the plots of Hotelling’s T² vs Q residuals.

Quali-quantitative monitoring of chemocatalytic cellulose conversion into lactic acid by FT-NIR spectroscopy

Chemocatalytic conversion of cellulose into lactic acid is a valuable alternative to simple sugar fermentation. Nevertheless, the procedures still need optimization to be translated to the industrial scale. Such translation would benefit by on-line monitoring of reaction parameters by fast, inexpensive, direct spectroscopic techniques. In this work, we propose the application of FT-NIR spectroscopy as a suitable analytical tool for monitoring the chemocatalytic conversion of cellulose into lactic acid. Comparison between different FT-NIR spectra at different reaction temperatures and times was exploited to qualitatively indicate the feasibility of the reaction. Besides, an FT-NIR prediction model was proposed for rapidly estimating the molar distribution of cellulose catalytic degradation components in the reaction mixtures. The calibration model was based on reference samples analysed by HPLC. The model was validated by an external validation set. Relevant statistical values of Ratio Performance to Deviations (RPD) referred to both calibration and external validation were obtained, thus demonstrating the potential of such analytical technique in process monitoring.

In Situ Monitoring of Sugar Content in Breakfast Cereals Using a Novel FT-NIR Spectrometer

This research demonstrates simultaneous predictions of individual and total sugars in breakfast cereals using a novel, handheld near-infrared (NIR) spectroscopic sensor. This miniaturized, battery-operated unit based on Fourier Transform (FT)-NIR was used to collect spectra from both ground and intact breakfast cereal samples, followed by real-time wireless data transfer to a commercial tablet for chemometric processing. A total of 164 breakfast cereal samples (60 store-bought and 104 provided by a snack food company) were tested. Reference analysis for the individual (sucrose, glucose, and fructose) and total sugar contents used high-performance liquid chromatography (HPLC). Chemometric prediction models were generated using partial least square regression (PLSR) by combining the HPLC reference analysis data and FT-NIR spectra, and associated calibration models were externally validated through an independent data set. These multivariate models showed excellent correlation (Rpre ≥ 0.93) and low standard error of prediction (SEP ≤ 2.4 g/100 g) between the predicted and the measured sugar values. Analysis results from the FT-NIR data, confirmed by the reference techniques, showed that eight store-bought cereal samples out of 60 (13%) were not compliant with the total sugar content declaration. The results suggest that the FT-NIR prototype can provide reliable analysis for the snack food manufacturers for on-site analysis.

Quality evaluation of heat-treated sessile oak (Quercus petraea L.) wood by colour and FT-NIR spectroscopy

ABSTRACT The prediction of physical (wood density, mass loss) and mechanical (bending properties, compressive strength parallel to grain, and Brinell hardness) properties of heat-treated sessile oak (Quercus petraea L.) by FT-NIR spectroscopy, colour change, and partial least squares regression (PLS) was studied. Samples of oak sapwood and heartwood were treated for 4 h at temperatures at 170, 190, and 210°C. FT-NIR spectra (100 scans and 4 cm−1) and colour parameters (CIE L*a*b* system) were collected on the radial surface before and after heat treatment. The applied heat treatment changed wood properties, darkened the colour and reduced the colour difference between heartwood and sapwood. According to the residual prediction deviation (RPD), most models obtained could be used for preliminary wood quality screening (1.5 < RPD < 2.0) and the properties of heartwood were better predicted than those of sapwood. Mass loss prediction by colour change can be used for quality control while for the other properties FT-NIR provided a more clear picture of the heat-treated oak wood and can serve as a quality indicator.

FT-NIR assisted Machine and Deep learning for determination of Acteoside, Aucubin and Catalpol contents of Plantago lanceolata

Aucubin, acteoside and catalpol are important compounds present in this plant but their determination is time consuming, costly and necessitate the use of chemicals. In the present study we developed a method based on FT-NIR spectroscopy to determine aucubin, acteoside and catalpol from dry powders of Plantago lanceolata. FT-NIR spectra were processed in PLS and deep learning methods and the accuracies of models were calculated and compared. Aucubin, acteoside and catalpol contents were predicted with a root mean square error of 0.56, 0.25 and 0.18%, respectively by using PLS method. Deep learning did not allow to improve the accuracies but allowed obtaining similar accuracies. FT-NIR based method showed interesting results and is promising to develop a fully usable method for selection programs of plantain with high level contents of bioactive compounds.

Sweetness Detection and Grading of Peaches and Nectarines by Combining Short- and Long-Wave Fourier-Transform Near-Infrared Spectroscopy

Sweetness is one of the most important quality traits for fruit which directly affects consumers’ acceptance. The aim of this study was to detect and grade sweetness levels of intact peaches and nectarines by combining short-wave and long-wave Fourier-transform near-infrared spectroscopy (FT-NIRS). FT-NIR spectra of 720 samples (360 peaches and 360 nectarines) were collected using a system operated in range from 15,700 to 4000 cm−1. Soluble solids contents (SSC) were measured as indicators and samples were individually grouped into three sweetness levels based on predetermined thresholds. Partial least squares regression (PLSR) and principal component regression (PCR) were compared, and results showed that PLSR models developed using raw full spectra performed best with correlation coefficient (R p) of 0.7747 and 0.8473, and root mean square error (RMSEP) of 0.6915% and 0.7228% in prediction. Furthermore, effective wavelengths were individually selected using regression coefficients (RC) and competitive adaptive reweighted sampling (CARS) to simplify the PLSR models. Through comparison, RC-PLSR resulted in comparable performance with original models showing R p = 0.7716 and 0.8157, and RMSEP = 0.6943% and 0.7945%. To grade different sweetness levels, two-dimensional correlation spectroscopy (2D-COS) was innovatively conducted, and autopeaks characterizing the differences among different groups were retained. Partial least square discriminant analysis (PLS-DA) classification models based on autopeaks achieved a total correct classification rate (CCR) of 66.7% and 86.6% for peaches and nectarines, respectively. The overall results indicated the combination of short-wave and long-wave FT-NIRS was potentially useful in detecting and grading sweetness for these fruit varieties.

Characteristion of hydrogen bond of L–methionium hydrogen selenite by temperature dependent two-dimensional correlation FT-NIR spectroscopy

L–methionium hydrogen selenite (L–met∙H2SeO3) has been obtained from aqueous solution by slow solvent evaporation method at the room temperature and it has been identified by FTIR and NIR spectra. Two-dimensional correlation analysis (2DCOS) from temperature dependent FT-NIR spectra has showcased that the intensities of 5110, 5538, 5743 cm−1 bands for intermolecular hydrogen bonds decrease with increasing temperature, while that the intensities of 4290, 4370, 4655 cm−1 bands for intramolecular hydrogen bonds increase correspondingly. A differential thermal analysis has confirmed that L–methionium hydrogen selenite has more stable property and higher decomposition temperature compared with methionine.

Characterisation of flaxseed cultivars based on NIR diffusion reflectance spectra of whole seeds and derived samples

Discrimination of yellow and brown flaxseed cultivars was made based on diffusion reflectance FT-NIR spectra of whole seeds. The spectra of flaxseed kernels, hulls, defatted flours, and oils were also measured for comparison. Hierarchy cluster analysis (HCA) and principal component analysis (PCA) were used for the discrimination. Multivariate analyses of FT-NIR spectra led to satisfactory discrimination of all flaxseed cultivars of this study mainly according to the nutritionally important fatty acid composition that was confirmed by comparison with the corresponding spectra of flaxseed kernel and oil. By contrast, spectral features of proteins, polysaccharides, and tannins predominated in the FT-NIR spectra of flaxseed hulls and defatted flours.

Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy.

Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C-H, C-H2, C-H3), alkene group (-CH=CH-), hydroxyl group (O-H) and Amides I and II vibrational modes.