FT-NIR Spectroscopy Research Articles

Non-targeted NIR spectroscopy and SIMCA classification for commercial milk powder authentication: A study using eleven potential adulterants

A non-targeted detection method using near-infrared (NIR) spectroscopy combined with chemometric modeling was developed for the rapid screening of commercial milk powder (MP) products as authentic or potentially mixed with known and unknown adulterants. Two benchtop FT-NIR spectrometers and a handheld NIR device were evaluated for model development. The performance of SIMCA classification models was then validated using an independent test set of genuine MP samples and a set of gravimetrically prepared mixtures consisting of MPs spiked with each of eleven potential adulterants. Classification models yielded 100% sensitivities for the benchtop spectrometers. Better specificity, which was influenced by the nature of the adulterant, was obtained for the benchtop FT-NIR instruments than for the handheld NIR device, which suffered from lower spectral resolution and a narrower spectral range. FT-NIR spectroscopy and SIMCA classification models show promise for the rapid screening of commercial MPs for the detection of potential adulteration.

Near infrared spectroscopy coupled with chemometric algorithms for predicting chemical components in black goji berries (Lycium ruthenicum Murr.)

Fourier-transform near infrared spectroscopy coupled with chemometric algorithms was applied comparatively for the quantification of chemical compositions in black wolfberry. The compositional parameters, i.e. total flavonoid content, total anthocyanin content, total carotenoid content, total sugar, and total acid were performed for quantification. Model results were evaluated using the correlation coefficients of determination for calibration (R2) and prediction (r2), root-mean-square error of prediction and residual predictive deviation. The findings revealed that the performances of models based on variable selection such as synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS were better than the classical PLS. The performance of the developed models yielded 0.88 ≤ R2 ≤ 0.97, 0.87 ≤ r2 ≤ 0.94 and 1.75 ≤ RPD ≤ 4.00. The overall results showed that the FT-NIR spectroscopy in conjunction with chemometric algorithms could be used for the quantification of the chemical composition of black wolfberry samples.

Identification and differentiation of insect infested rice grains varieties with FTNIR spectroscopy and hierarchical cluster analysis

The potential and practicality of FTNIR as a screening tool, with ward’s algorithms, was performed for two different varieties of rice namely, ‘badshah bhog’ and ‘swarna’, followed by cluster, dendrogram, histogram, and conformity analysis with different storage periods (0–225) and insect infestation. Dendrogram analysis resulted in a clear differentiation between infested rice varieties with non-infested ones while hierarchical cluster analysis, lead to detection of different levels of infestations. Histograms analysis of averaged FTINR spectra of rice grains samples provided 100% classification between infested and non-infested samples. Dissimilarities between rice grains were calculated using Pearson’s correlation coefficients which were further converted to D values, and heterogeneity among the different varieties of rice along with a different level of infestation was identified. The results further revealed that the percentage accuracy (%) of classification for badshah bhog varied from 93.10 to 98.84%, while that for the swarna rice was between 95.75 and 99.74%.

Monitoring alcohol concentration and residual glucose in solid state fermentation of ethanol using FT-NIR spectroscopy and L1-PLS regression

This study aimed to investigate the potential of FT-NIR spectroscopy technique combined with chemometrics method, which employed to monitor time-related changes of alcohol concentration and residual glucose during solid state fermentation (SSF) of ethanol. Characteristic wavelength variables were firstly selected by use of L1-norm regularization approach. Then, the partial least squares (PLS) regression model was finally developed using the variables selected by L1-norm regularization method to quantitative determine alcohol concentration and residual glucose in SSF of ethanol. Compared with the best results of full-spectrum PLS, the L1-PLS model obtained better results as follows: RMSECV = 1.0392 g/L, Rc = 0.9911, RMSEP = 1.0910 g/L, Rp = 0.9917 for alcohol concentration; RMSECV = 1.7002 g/L, Rc = 0.9880, RMSEP = 2.1859 g/L, Rp = 0.9896 for residual glucose. The overall results sufficiently demonstrate that FT-NIR spectroscopy technique coupled with appropriate chemometrics method is a promising tool for monitoring the process of SSF of ethanol.

An exploratory study for the technological classification of egg white powders based on infrared spectroscopy

This work aims at the evaluation of FT-NIR and FT-IR spectroscopy as rapid, easy, and cost-effective tools for the classification of egg white powder (EWP) based on its technological properties. Up to 100 commercial spray-dried EWP samples with known gelling and foaming properties were used to acquire FT-NIR and FT-IR spectra. An appropriate data-splitting algorithm (Duplex) was applied in order to create, for each dataset, a calibration set and a representative validation test set for prediction. Different spectral pre-treatments and their combinations were investigated for the calculation of Partial Least Squares–Discriminant Analysis models in order to classify samples according to gel strength, foam height, and foam instability. A variable selection strategy based on the so-called Variable Importance in Projection scores was also evaluated. Both FT-NIR and FT-IR spectroscopy showed good potential in discriminating EWP samples with different technological properties. Correct classification percentages in prediction ranging from 59% to 89% were obtained with the best models calculated with selected wavenumbers. These results show a promising industrial perspective, demonstrating the possibility of developing cheap and fast instruments spanning a limited spectral range, which can be implemented on the production lines for EWP sorting and quality control.

Rapid, simultaneous and non-destructive assessment of the moisture, water activity, firmness and SO2 content of the intact sulphured-dried apricots using FT-NIRS and chemometrics

The potential of using FT-NIR spectroscopy for the rapid and non-destructive measurement of the moisture, water activity, firmness and SO2 content of the intact sulphured-dried apricots (SDA) was investigated for the first time in the literature. The partial least squares regression (PLS-R) models constructed using FT-NIR spectra were very successful in predicting the moisture content (R2p = 0.986, RMSEP = 1.22%, RPD = 9.15) and water activity (R2p = 0.987, RMSEP = 0.016, RPD = 9.37) of SDAs. Satisfactory results were also obtained for the models developed for the prediction of the firmness (R2p = 0.845, RMSEP = 0.445, RPD = 2.55) and SO2 content (R2p = 0.804, RMSEP = 349 mg kg−1, RPD = 2.27). These results clearly demonstrate that the major quality parameters of SDA can be simultaneously measured in a short time by FT-NIR spectroscopy without any need for the sample preparation or skilled laboratory personnel.

Quantification of sterols and fatty acids of extra virgin olive oils by FT-NIR spectroscopy and multivariate statistical analyses

In this study FT-NIR spectra of 73 extra virgin olive oil (EVOO) samples obtained from 21 different cultivars and 4 geographic regions were used to develop partial least squares regression (PLS-R) models for the rapid quantification of sterols for the first time in the literature. The results of the model validation showed that the total sterol content of the EVOO samples could be predicted with good prediction ability (Rp2 = 0.839, RMSEP = 192 mg/kg, RPD = 2.64). However, the prediction models for the individual sterol forms performed poorly. Except for heptadecanoic and eicosenic acids, models with good prediction ability could be established for the quantification of the major fatty acids found in EVOOs with Rp2 and RPD values ranging between 0.716-0.997 and 2.02–17.6, respectively. Even better model performances were obtained when fatty acids were grouped according to their unsaturation degree as SFA (Rp2 = 0.998, RMSEP = 0.102%, RPD = 21.8), MUFA (Rp2 = 0.997, RMSEP = 0.255%, RPD = 18.7) and PUFA (Rp2 = 0.998, RMSEP = 0.147%, RPD = 25.1).

Energy characteristics assessment of olive pomace by means of FT-NIR spectroscopy

Biomass is an important renewable energy source, in particular if obtained by residues it becomes even more sustainable. In Italy, residual biomass coming from olive oil industry, i.e. olive pomace, is produced in a significant amount and is concentrated in olive oil extraction sites, making interesting a possible valorisation of these residues. The different extraction processes employed influence the pomace quality and, consequently, it is fundamental to find a rapid technique to assess its physical and chemical characteristics for a correct valorisation. The aim of the work was to develop a NIR-based methodology to obtain in a fast and cheap way information about olive pomace. Several samples (n = 104) were collected in Marche region and analysed according to standards methods. NIR spectra were acquired using both fiberoptic probe and integrating sphere and subsequently were elaborated with multivariate techniques, i.e. principal component analysis (PCA) and partial least square regression (PLS). Results show that information on extraction process and composition of the pomace can be obtained. Prediction models with performance suitable for quality control applications were obtained for moisture and ash contents, whereas gross calorific value model was suitable only for screening application.

Prediction of amino acids, caffeine, theaflavins and water extract in black tea using FT-NIR spectroscopy coupled chemometrics algorithms

We have described the in situ prediction of amino acids, caffeine, theaflavins and water extract in black tea using FT-NIR coupled various variable selection algorithms.

Quantitative analysis of soil nutrition based on FT-NIR spectroscopy integrated with BP neural deep learning

The algorithmic scheme of the BPN-DL framework (details of eachHkare presented in the hexagonal box).

Quantification of Condensed Tannins in Sainfoin Powder (Onobrychisviciifolia) by FT-NIR Spectroscopy

Models based on FT-NIR spectroscopy and PLS-regressions were developed over three harvest years to determine the condensed tannins contents (CT, %(w/w)) in sainfoin powders. The three data sets corresponding to the three harvest years were used as calibration and then validation sets, successively. Finally, a global model gathering all the three years data sets has been developed. The developed models predict CT in a range of 2.06 to 11.28 %(w/w). The accuracy of the models depended on the range of CT-values of the calibration and validation sets. Finally, it was possible to predict the CT with a SEP-value lower than 0.5% (w/w) and R2-value higher than 0.9. In the present study, some of the PLS-parameters such as bias, slope and SEP have been statistically evaluated using the international standard ISO 12099:2017. The final global model was very promising since bias was not significantly different from 0, the slope was not significantly different from 1, and obtained SEP was 0.49% (w/w) while the calculated SEP-limit was 0.47% (w/w). The presently developed model was robust over the three years and the global model presented very interesting values. Such approach would be very useful to develop a new quantitative, rapid and low cost method to assess the CT of sainfoin powders. This method will allow us to free ourselves from the traditional chemical method that consumes time, money and chemicals.

Determining quality parameters of fish oils by means of 1H nuclear magnetic resonance, mid-infrared, and near-infrared spectroscopy in combination with multivariate statistics

Fish oil is becoming increasingly popular as a dietary supplement as well as for its use in animal feed, which is mainly due to its high contents of the health promoting omega-3 fatty acids. However, these polyunsaturated fatty acids are highly susceptible to oxidation, which results in a decrease of the fish oil quality. This study investigated the potential of 1H NMR, FT-MIR, and FT-NIR spectroscopy in the quality assessment of fish oils. A total of 84 different fish oils, of which 22 were subjected to accelerated storage with varying temperature and light exposure, were used to develop models for predicting the peroxide value (PV), the anisidine value (AnV), and the acid value (AV). Predictions were based on comprehensive spectroscopic data in combination with Artificial Neural Networks (ANN) as well as Partial Least Squares Regression (PLSR). The best ANN model for PV was obtained from NMR data, with a predictive coefficient of determination (Q2) of 0.961 and a Root Mean Square Error of Prediction (RMSEP) of 1.5meqO2kg−1. The combined MIR/NIR data provided the most reliable ANN model for AnV (Q2=0.993; RMSEP=0.74). For AV, the ANN model based on the MIR data yielded a Q2 of 0.988 and an RMSEP of 0.43mgNaOHg−1. In most cases, the accuracy of the ANN models was superior to the respective PLSR models. Variable selection and data dimensionality reduction turned out to improve the performance of the ANN models in some cases. The application of 1H NMR, FT-MIR, and FT-NIR spectroscopy in combination with ANN can be considered very promising for a rapid, reliable, and sustainable assessment of fish oil quality.

Rapid Assessment of Quality Change and Insect Infestation in Stored Wheat Grain Using FT-NIR Spectroscopy and Chemometrics

Major qualitative changes during storage of wheat are attributed to infestation by weevils, beetles, and moths. Alteration in inherent macro and micro nutrients and corresponding reduction in grain mass is the ultimate indicator of the deteriorations. Sitophilus oryzae and Ryzopertha dominica are two commonly found insects in stored wheat, which cause the major losses by feeding upon the grain mass and contaminating the grain bulk with their metabolic waste. The current study focused on development of a rapid and non-destructive FT-NIR spectroscopic method for the determination of insect infestation by analyzing the quality changes in grain due to infestation. A total of 128 wheat samples of varying moisture content, insect count, and storage days were analyzed for quality parameters. FT-NIR library was generated and the spectral data were analyzed using partial least squares regression (PLS) with various preprocessing techniques. The best models for properties with lowest root mean square error of cross-validation values for moisture, protein, uric acid, 1000 kernel weight, and hardness were 0.485, 0.248, 2.58, 0.576, and 0.762, respectively. R 2 obtained for the abovesaid quality parameters were 0.901, 0.938, 0.895, 0.907, and 0.912 demonstrating good fit of the PLS models. The developed methods will be very much useful for storage godowns, bakery industries, graders, and exporters providing rapid, reliable, and precise quality estimation during reception of the raw material without involvement of hazardous chemicals.

Prediction of gross calorific value and ash content of woodchip samples by means of FT-NIR spectroscopy

The use of woodchip, and biofuels in general, is a fundamental step towards the transition from fossil fuel to renewable energy. The growth in the demand for wood fuels and the inherent variability in the properties of woody material lead to the need to verify its quality. EN ISO 17225-4 divides woodchip in different quality classes according to chemical-physical parameters and quality attributes.In this study, we have coupled near infrared spectroscopy with Partial Least Square regression to model gross calorific value and ash content of woodchip samples. Moreover, variables selection methods were tested in order to improve the models and get better prediction.Gross calorific value and ash content were predicted with a standard error of 234J/g and 0.44%, respectively. The results indicate that the models could be used in screening applications and near infrared spectroscopy is a promising tool in the evaluation of biomass quality.

Identification of coffee leaves using FT-NIR spectroscopy and SIMCA

Abundant literature has been devoted to coffee beans (green or roasted) chemical description but relatively few studies have been devoted to coffee leaves. Given the fact that coffee leaves are used for food and medicinal consumption, it was of interest to develop a rapid screening method in order to identify coffee leaves taxa. Investigation by Fourier – Transform near infrared spectroscopy (FT-NIRS) was performed on nine Coffea taxa leaves harvested over one year in a tropical greenhouse of the Botanic Garden Meise (Belgium). The only process after leaves harvesting was an effective drying and a homogeneous leaves grinding. FT-NIRS with SIMCA analysis allowed to discriminate the spectral profiles across taxon, aging stage (mature and senescence coffee leaves) and harvest period. This study showed that it was possible (i) to classify the different taxa, (ii) to identify their aging stage and (iii) to identify the harvest period for the mature stage with a correct classification rate of 99%, 100% and 90%, respectively.

Microstructure aspects of radiation-cured networks: Cationically polymerized aromatic epoxy resins

The thermo-mechanical properties and nanostructural features of epoxy aromatic resins cationically cured by UV–visible or electron beam radiation have been studied by FT-NIR spectroscopy, dynamic mechanical analysis (DMA), dielectric spectroscopy (DS), and atomic force microscopy (AFM). The influence of formulation (nature and content of onium salt) and of curing parameters (doses, thermal treatment) on the thermophysical have been investigated. The presence of several relaxation domains observed by DMA and DS analysis confirms the presence of heterogeneities in the cured materials. Network formation is described by the percolation of glassy nanoclusters which are evidenced by AFM analyses. AFM probing by quantitative nanomechanical measurements confirms the gradual build-up of the local Young's modulus in good agreement with the macroscopic value.

FT-NIRS coupled with chemometric methods as a rapid alternative tool for the detection & quantification of cow milk adulteration in camel milk samples

In the current study FT-NIRS combined with multivariate methods was developed as a novel alternative tool for the rapid determination of cow milk adulteration in camel milk samples. For this investigation the camel milk as well as cow milk samples were collected from Aldhahira and Sharqia regions of Sultanate of Oman. FT-NIR spectroscopy was used for the measurement of all milk samples with a scanning range from 4000 to 10000cm−1 in absorption mode at 2cm−1 resolution and using a 0.2mm path length CaF2 sealed cell. The chemometric methods like Principle component analysis (PCA), partial least discriminant analysis (PLS-DA) and partial least regression analysis (PLS) were used for statistical analysis of the obtained NIR spectral data. PLS-DA model was built to check the discrimination between the pure and adulterated camel milk samples. For PLS-DA model the R-square value obtained was 0.97 with 0.080 RMSEC (Root mean square error). Furthermore, PLS regression model was also built to quantify the levels of cow milk adulterant in camel milk samples. The PLS regression model was obtained with the R-square value of 92%. Root mean square error of cross validation (RMSECV) value for PLSR was found 1.76% showing good prediction with root mean square error of prediction (RMSEP) value of 1.32%. This newly developed method is non-destructive, quick, no need of much sample preparation and having detection limit less than 1.5%.

Prediction of local and global tomato texture and quality by FT-NIR spectroscopy and chemometric

Prediction of local and global tomato texture and quality by FT-NIR spectroscopy and chemometric

Manipulation of Mg2+-Ca2+ Switch on the Development of Bone Mimetic Hydroxyapatite.

Ionic substitution can affect essential physicochemical properties leading to a specific biological behavior upon implantation. Therefore, it has been proposed as a tool to increase the biological efficiency of calcium phosphate based materials. In the following study, we have evaluated the contribution of an important cation in nature, Mg2+, into the structure of previously studied biocompatible and biodegradable hydroxyapatite (HA) nanorods and its subsequent effect on its chemical, morphology, and bone mimetic articulation. Mg2+-substituted HA samples were synthesized by an aqueous wet-chemical precipitation method, followed by an hydrothermal treatment involving a Mg2+ precursor that partially replace Ca2+ ions into HA crystal lattice; Mg2+ concentrations were modulated to obtain a nominal composition similar to that exists in calcified tissues. Hydrothermally synthesized Mg2+-substituted HA nanoparticles were characterized by X-ray powder diffraction, FT-NIR and EDX spectroscopies, field emission scanning and high resolution transmission electron microscopies (FE-SEM, H-TEM). Molecular modeling combining ab initio methods and power diffraction data were also performed. Results showed that Mg2+-substitution promoted the formation of calcium deficient HA (cdHA) where Mg2+ replacement is energetically favored at Ca(1) position in a limited and specific amount directing the additional Mg2+ toward the surface of the crystal. The control of Mg2+ incorporation into HA nanorods gave rise to a tailored crystallinity degree, cell parameters, morphology, surface hydration, solubility, and degradation properties in a dose-replacement dependent manner. The obtained materials show qualities that conjugated together to drive an optimal in vitro cellular viability, spreading, and proliferation confirming their biocompatibility. In addition, an improved adhesion of osteoblast was evidenced after Mg2+-Ca2+ substitution.

A comprehensive quality evaluation method by FT-NIR spectroscopy and chemometric: Fine classification and untargeted authentication against multiple frauds for Chinese Ganoderma lucidum

The origins and authenticity against frauds are two essential aspects of food quality. In this work, a comprehensive quality evaluation method by FT-NIR spectroscopy and chemometrics were suggested to address the geographical origins and authentication of Chinese Ganoderma lucidum (GL). Classification for 25 groups of GL samples (7 common species from 15 producing areas) was performed using near-infrared spectroscopy and interval-combination One-Versus-One least squares support vector machine (IC-OVO-LS-SVM). Untargeted analysis of 4 adulterants of cheaper mushrooms was performed by one-class partial least squares (OCPLS) modeling for each of the 7 GL species. After outlier diagnosis and comparing the influences of different preprocessing methods and spectral intervals on classification, IC-OVO-LS-SVM with standard normal variate (SNV) spectra obtained a total classification accuracy of 0.9317, an average sensitivity and specificity of 0.9306 and 0.9971, respectively. With SNV or second-order derivative (D2) spectra, OCPLS could detect at least 2% or more doping levels of adulterants for 5 of the 7 GL species and 5% or more doping levels for the other 2 GL species. This study demonstrates the feasibility of using new chemometrics and NIR spectroscopy for fine classification of GL geographical origins and species as well as for untargeted analysis of multiple adulterants.