Fourier Transform Near-infrared Spectrometer Research Articles

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement.

By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein-nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites.

Rapid Determination of the Total Petroleum Hydrocarbon Content of Soils by Handheld Fourier Transform Near-Infrared Spectroscopy.

For successful soil remediation and hydrocarbon exploration operations, determining the total petroleum hydrocarbon (TPH) content of soils is an indispensable process step. This paper reports on the performance of a handheld Fourier transform near-infrared (FT-NIR) spectrometer for rapid and quantitative determination of TPH content of soils from two different sites by diffuse reflection measurements. For rapid decisions for exploration work or environmental site assessment projects, a quick─preferably on-site─determination of TPH content is valuable. Diffuse reflection NIR spectra were recorded from soil samples of two different sites with TPH reference values ranging from 350 to 30,000 ppm, as determined by capillary gas chromatography and flame ionization detection with hydrocarbon fingerprinting C1-C44. However, this paper not only addresses the development of site-specific partial-least squares (PLS) calibrations but also demonstrates the locally-weighted PLS (LW-PLS) technique, which can be used to develop global, site-independent PLS calibrations without significant penalty in calibration performance. As a first step, the diffuse reflection spectra were used to develop conservative, site-specific PLS calibration models with root-mean-square calibration/cross-validation errors (RMSEC/RMSECV) of 1043/1106 and 741/785 ppm TPH, respectively, and the average absolute prediction errors for samples not contained in the calibration set were 451 and 293 ppm for the two sites, respectively. In a further step, significant degradation of the RMSE values of a conservative PLS model based on the NIR spectra of both sites was then compared to the application of the LW-PLS method, with only a slight loss of the prediction accuracy relative to the site-independent models. This study confirms the ability of next-generation portable FT-NIR spectrometers to predict low TPH levels in various soil types through both─soil-specific and site-independent─calibrations, giving these spectrometers the potential to become rapid screening tools in the field.

Accurate Classification of Chunmee Tea Grade Using NIR Spectroscopy and Fuzzy Maximum Uncertainty Linear Discriminant Analysis.

The grade of tea is closely related to tea quality, so the identification of tea grade is an important task. In order to improve the identification capability of the tea grade system, a fuzzy maximum uncertainty linear discriminant analysis (FMLDA) methodology was proposed based on maximum uncertainty linear discriminant analysis (MLDA). Based on FMLDA, a tea grade recognition system was established for the grade recognition of Chunmee tea. The process of this system is as follows: firstly, the near-infrared (NIR) spectra of Chunmee tea were collected using a Fourier transform NIR spectrometer. Next, the spectra were preprocessed using standard normal variables (SNV). Then, direct linear discriminant analysis (DLDA), maximum uncertainty linear discriminant analysis (MLDA), and FMLDA were used for feature extraction of the spectra, respectively. Finally, the k-nearest neighbor (KNN) classifier was applied to classify the spectra. The k in KNN and the fuzzy coefficient, m, were discussed in the experiment. The experimental results showed that when k = 1 and m = 2.7 or 2.8, the accuracy of the FMLDA could reach 98.15%, which was better than the other two feature extraction methods. Therefore, FMLDA combined with NIR technology is an effective method in the identification of tea grade.

Rapid measurement of classification levels of primary macronutrients in durian (Durio zibethinus Murray CV. Mon Thong) leaves using FT-NIR spectrometer and comparing the effect of imbalanced and balanced data for modelling

For durian growth to produce high-quality fruit, plants should receive sufficient nutrients. Currently, farmers apply various fertilisers to produce a large quantity and quality of durian fruit, irrespective of the actual nutrients that the plant requires. Accordingly, the production cost is high and non-renewable resources. Therefore, this study focused on rapid classification primary macronutrient levels in durian (Durio zibethinus Murray CV. Mon Thong) leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and investigated the effect of imbalanced data on efficient classification models. Contents of N, P, and K in durian leaves were measured via NIR with the wavelength range of 800–2,500 nm. Classification models were developed using partial least squares, k-nearest neighbour, and artificial neural networks (ANNs) with imbalanced and balanced data. The imbalanced data were balanced using a synthetic minority oversampling technique (SMOTE). In this study, the model regarding the fresh leaf sample performed better than that for the dried ground leaf sample. Moreover, the ANN was the best algorithm, exhibiting validation accuracies of classified levels corresponding to N = 0.99 and P = 0.97 when the data were analysed with SMOTE and K = 1.00 from the original balanced data. The imbalanced data affected biased classification when the models could increase the classification accuracy by applying balanced data for modelling.

Algorithm and hyperparameter optimizations for hetero-device classification by near-infrared spectra of falsified and substandard amoxicillin capsules

In this work, we optimized classification algorithms and the hyperparameters for screening falsified and substandard amoxicillin capsules. The distribution of low-quality medical products is a serious problem, especially in low- and middle-income countries. Near-infrared (NIR) spectroscopy has been proposed as the first choice for a screening device. However, preparation of the reference library for the classification training is a highly difficult process. We herein propose a hetero-device classification between training and test devices. In this proposal, Fourier-transform NIR spectrometer and portable wavelength dispersive NIR spectrometer were used as training and test devices, respectively. As the classifier candidates, we examined 13 algorithms and selected 8. We then optimized the hyperparameters for these classifiers by the grid search and cross validation methods. In the final analysis, few classifiers were found to give acceptable prediction results by the hetero-device classification. When using these methods, it is crucial to examine the results by the classification probability, due to the trade-off between sensitivity and specificity. Finally, we suggest that k-nearest neighbors, extra trees, and gradient boosting classifiers are the optimal algorithms with high classification probability for the substandard and falsified amoxicillin capsules.

Determination of aflatoxin B1 value in corn based on Fourier transform near-infrared spectroscopy: Comparison of optimization effect of characteristic wavelengths

Aflatoxin B1 (AFB1) is an important cause of human liver cancer. This study proposes a quantitative detection method for the AFB1 in corn based on Fourier transform near-infrared (FT-NIR) spectroscopy technology. First, we acquired spectral data of corn samples with different degrees of mildew using FT-NIR spectrometer; then, we used the ant colony optimization (ACO) and the NSGA-Ⅱ algorithms to optimize the characteristic wavelengths of the spectra after SNV treatment respectively; Finally, the back propagation neural network (BPNN) models were established using the optimized characteristic wavelengths to realize the accurate detection of the AFB1 in corn. The results obtained showed that the prediction performance of the BPNN model established using the four characteristic wavelength variables optimized by NSGA-Ⅱ algorithm is the best, and the best NSGA-Ⅱ-BPNN model's correlation coefficient of prediction (RP) is 0.9951, the root mean square error of prediction (RMSEP) is 1.5606 μg ⋅ kg−1. The overall results demonstrate that the quantitative detection of AFB1 in corn by FT-NIR technique is feasible; in addition, the NSGA-Ⅱ algorithm has its unique advantages in the optimization of spectral characteristics, and it can obtain characteristic wavelength variables with strong pertinence and a small number.

Fourier transform near-infrared spectroscopy coupled with variable selection methods for fast determination of salmon fillets storage time

To address the fast and nondestructive determination of salmon fillets storage time associated with its freshness, Fourier transform near-infrared (FT-NIR) spectroscopy coupled with advanced variable selection methods was attempted in this work. Fresh salmon fillets were divided into three groups and stored at -18℃, 4℃ and 20℃, respectively. The spectra under each temperature were collected by a FT-NIR spectrometer with the range of 4000–11000 cm−1. Advanced variable selection methods, including random frog (RF), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), CARS combined with SPA (CARS-SPA) and variable combination population analysis combined with iteratively retaining informative variables (VCPA-IRIV), were utilized to extract key feature variables and shrink variable space. The prediction models were constructed by using partial least squares regression (PLSR) based on full spectral variables and selected feature variables. Variable selection method VCPA-IRIV coupled with PLSR (VCPA-IRIV-PLSR) showed the best prediction performance in each temperature, with determination coefficients of prediction set (R2P) of 0.9988, 0.9976 and 0.9998, and root mean square error of prediction set (RMSEP) of 0.145, 0.209 and 0.024 for -18℃, 4℃ and 20℃, respectively. The overall results showed the great feasibility of FT-NIR combined with variable selection methods in the rapidly, nondestructively and accurately predicting freshness of salmon fillets stored at different temperatures.

Quantification of Carbon Dioxide (CO2), Methane (CH4), and Nitrous Oxide (N2O) Using Near Infrared Spectroscopy and Multivariate Calibration in High Humidity Levels

In this work we developed a promising analytical method combining Fourier transform nearinfrared (FT-NIR) spectroscopic technique and first-order multivariate calibration using partial least-squares (PLS) model to simultaneously quantify the main greenhouse gases (GHG’s): methane (CH4), carbon dioxide (CO2), nitrous oxide (N2O) and water vapor (H2O). The models were built using 70 mixtures with different concentration of these gases, 0.25-32.0 ppm to CH4 and N2O, and 50-1100 ppm to CO2 and different values of relative humidity (52-85%, 20 ºC) in synthetic air. After preparing each of the mixtures, they were analyzed by using FT-NIR and a reference analytical technique based on gas chromatography with mass spectrometric detection (GC-MS). The FT-NIR spectrometer was coupled with a long optical path cell, with 105.6 meters of optical path. In sequence, the spectra of all mixtures and its concentration values for each gas were used to build the multivariate calibration models, using PLS regressions. For this, the mixtures were grouped with Kennard Stone algorithm, 50 samples to calibration set and 20 samples to prediction set. The values of RMSEP (root mean square error of prediction) obtained for each model are 0.66, 28.7 and 0.66 ppm, respectively, for CH4, CO2, and N2O. The limits of quantification (LOQ) for each PLS models are 0.26, 3.6, and 0.99 ppm, respectively, for CH4, CO2, and N2O. The results show the potentiality of application of this system to monitoring emission sources in which the concentration of these gases are relatively high, as urban centers, industrial areas, and landfills.

Near Infrared Spectroscopy: Applications In Pharmaceutical Industry

This work explores the comprehensive development of a near infrared (NIR) spectroscopic analytical technique for high-throughput applications for the determination of pharmaceutical raw materials and excipients in a pharmaceutical formulation. Advancements in the NIR spectrometry as quality and process control tool have been discussed. The applications include analyses of different types of samples, manufacturing and identification of active pharmaceutical ingredients based on specified experimental design. The samples were measured in diffusion or reflectance mode in a fourier-transform NIR spectrometer. Additionally, chemometric software was employed for quantitative interpretation of bands. The spectrometry can be utilized for identification of raw materials, their size, polymorphism and blend homogeneity.

Using near-infrared spectroscopy to determine moisture content, gel strength, and viscosity of gelatin

Gelatin is a natural biopolymer obtained by denaturation and partial hydrolysis of fibrous collagen, which is a fibrous protein present in the connective tissues of all species in the animal kingdom. Gelatin is used in many different industries, such as food, pharmaceuticals, photography, and cosmetics. The control of its physical properties is therefore of primary economic and technical importance for the gelatin industry, in particular because drying gelatin is the most expensive step in the manufacturing process and because customers demand products with well-controlled moisture content, gel strength, and viscosity. Consequently, a tool that provides rapid and nondestructive, real time analysis of gelatin would provide a definite competitive advantage. One such tool may be near-infrared (NIR) spectroscopy, which is a well-known analytical technique to determine the moisture content of complex matrices. We therefore use this technique to determine the moisture content, gel strength, and viscosity of 135 industrial gelatins. NIR spectra were acquired by using in reflectance mode a Fourier-transform NIR spectrometer with an integrating sphere to make a chemometric study of the industrial gelatins to determine the accuracy with which this technique can estimate moisture content, gel strength, and viscosity. The predictive accuracy for these parameters is quantified by using the ratio of performance of deviation (RPD), which gives 8.8, 3.7, and 2.1 in cross-validation, and 3.9, 1.8, and 1.9 in testing, respectively. Finally, we discuss the use of this technique in industrial conditions.

A rapid method to detect green pea and peanut adulteration in pistachio by using portable FT-MIR and FT-NIR spectroscopy combined with chemometrics

In this research, the determination of green pea and peanut adulteration in pistachio by portable Fourier transform mid-infrared (FT-MIR) and Fourier transform near-infrared (FT-NIR) spectroscopy was studied. A rapid method to detect adulteration in pistachio is a necessity in the food industry because of its high commodity value and being one of the most consumed tree nuts. Pistachio nut is a target for economically motivated adulteration, and green pea, spinach, and peanut are the most seen adulterants in pistachio. Portable FT-MIR and FT-NIR spectrometers are simple, non-invasive, rapid, easy to transport, and cheaper alternatives to traditional methods, including chromatography (GC, HPLC), mass-spectroscopy (GC-MS, LC-MS) for the detection of pistachio adulteration. Powdered pistachio samples were adulterated with powdered green pea and peanut samples at different concentrations (5–40%). Spectra were collected with a portable FT-MIR and FT-NIR spectrometers and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate a classification model to authenticate pure pistachio, and Partial Least Square Regression (PLSR) to predict the levels of adulterants in pistachio. SIMCA provided, in both units, very distinct clusters for pure and adulterated samples with inter-class distance (ICD) over 3. Both units also showed superior performance in predicting adulterant levels with rVal>0.99, standard error of prediction (SEP) < 2.5%. Based on SIMCA and PLSR models, portable FT-NIR spectrometer provided more advantages over portable FT-MIR unit because the prediction with the FT-NIR spectrometer was more precise than the FT-MIR unit. Both portable FT-MIR and FT-NIR units can be used as alternative methods to traditional methods and showed great potential for real-time surveillance to detect green pea and peanut adulteration in pistachio.

Rapid online determination of feed concentration in nitroguanidine spray drying process by near infrared spectroscopy

This work investigated the feasibility of using near-infrared (NIR) spectroscopy to rapidly online detect the feed concentration of nitroguanidine (NQ) in order to ensure the uniformity and stability of the NQ feed during the spray drying process. A Fourier transform NIR spectrometer with a detector installed on the NQ slurry pipeline was used to collect original spectra of NQ samples with different concentrations. Standard normal variate was used to preprocess the original spectra, and a quantitative model with 7 factors of feed concentration was established by partial least squares regression in the spectral wavenumber ranges of 6890–6444 cm−1 and 5126–4236 cm−1. The determination coefficients of calibration and prediction (Rc2,Rp2) were 0.9945 and 0.9880, the root mean square errors of calibration and prediction (RMSEC, RMSEP) were 0.111 and 0.157, and the residual predictive deviation (RPD) values of calibration and prediction were 13.5 and 9.18, respectively. The established model was applied to detect the feed concentration of 8 other samples, and the difference between the near-infrared prediction values and the traditional measured values was only 0.29%. The results indicate the excellent predictive performance and repeatability of the model and provide new insights into the NIR spectroscopy rapid online detection of the feed concentration of NQ during spray drying process.

Investigating aquaphotomics for temperature-independent prediction of soluble solids content of pure apple juice

The methods of aquaphotomics were explored as an aid to improve near infrared spectroscopic predictive modelling of the soluble solids content of pure apple juice at different temperatures. The study focussed on the first overtone region of the O–H stretching vibration of water (1300–1600 nm). A transmission-based FT-NIR (Fourier transform near infrared) spectrometer was used to acquire 103 spectra of freshly expressed juice samples from individual ‘Braeburn’ apples over the wavelength range of 870–1800 nm with a 1 mm cuvette at three temperatures, 20, 25 and 30°C. The aquagram of the first overtone water region showed a trend of increasing bound water absorption with rising soluble solids content, from 7.3 to 13.7°Brix, and increasing free water absorption with rising temperature from 20 to 30°C. Predictive models for apple juice soluble solids content at 25°C were developed using partial least squares regression with spectral pre-processing by standard normal variate (SNV) followed by second derivative transformation (SNV + 2D) or no pre-processing on absorbance spectra at all. The best result, with lowest standard error of prediction of 0.38°Brix, was obtained using the first overtone water region with partial least squares regression on the SNV + 2D spectra. The method of extended multiplicative scatter correction was used, as an additional pre-processing step, to improve apple juice soluble solids content prediction at different temperatures. The interference component selected for the extended multiplicative scatter correction method was the first principal component loading measured using pure water samples taken at the same three temperatures (20, 25 and 30°C). Such extended multiplicative scatter correction pre-processing greatly reduced the soluble solids content prediction bias, when applying the partial least squares regression model developed at 20°C to samples measured at 25 and 30°C, from 0.23 to 0.08 and 0.36 to 0.13°Brix, respectively. Model precision (in terms of standard error of prediction) was also slightly improved by 0.02°Brix in each case, from 0.40 to 0.38 and 0.46 to 0.44°Brix at 25 and 30°C respectively.

Determination of creatinine level in patient blood samples by Fourier NIR spectroscopy and multivariate analysis in comparison with biochemical assay

In this paper, in vivo spectra from 23 patients’ blood samples with various Creatinine (Cr) concentration levels ranging from 0.96 to 12.5 mg/dL were measured using Fourier transform near-infrared spectrometer (FT-NIRS) and spectrum quantitative analysis method. Since Cr undergoes passive filtration, it serves as a key biomarker of kidneys function via the estimation of glomerular filtration rate. Thus, increased blood Cr concentration reflects impaired renal function. After spectra pre-processing and outlier exclusion, a spectral model was developed based on partial least squares regression (PLSR) method, wherein Cr concentrations correlated with filtered NIR spectra across several peaks, where Cr is known to absorb NIR light. Several statistical metrics were applied to estimate the model efficiency during data analysis. Comparison of spectra-derived concentrations to reference Cr measurements by the current gold-standard Jaffe’s method held in hospital lab revealed a Cr prediction accuracy of 1.64[Formula: see text]mg/dL with good correlation of [Formula: see text]. Bland-Altman plots were used to compare between our calculations and reference lab values and reveal minimal bias between the two. The finding presented the potential of FT-NIRS coupled with PLSR technique for Cr determination.

Evaluation of diesel exhaust fluid (DEF) using near-infrared spectroscopy and multivariate calibration

Diesel engines are responsible for high NOx emission rates in the atmosphere. The urea-selective catalytic reduction (SCR) system is one of the most promising techniques for NOx emission control. The SCR system uses a urea solution known as Diesel Exhaust Fluid (DEF) that contains 32.5% (v/v) of urea dissolved in deionized water. The quality control of the urea concentration in DEF, which directly affects the efficiency of the SCR system, follows the ISO 22241-2 protocol. A simple, practical and efficient methodology to evaluate DEF requirements that uses near-infrared (NIR) spectroscopy and multivariate calibration is presented in our work. For this, DEF commercial samples and mixtures containing urea and deionized water were analyzed in a Fourier Transform Near Infrared Spectrometer (FT-NIR) and in a MicroNIR handheld spectrometer. The urea concentration in the commercial samples and mixtures was quantified by refractive index, according to ISO 22241-2. Partial Least Squares (PLS) models were developed to predict urea content. Variables selection techniques, such as Interval Partial Least Squares (iPLS) and Jack-knife, were tested. The handheld NIR instrument performed well and provided PLS models with RMSEP values statistically equivalent to the PLS models developed in the benchtop instrument. All the RMSEP values obtained were lower than the reproducibility of the ISO protocol. A feasible calibration transfer approach between the benchtop and the handheld instrument was demonstrated. The described methodology, aimed at in-field use for surveillance and DEF quality control purposes, represents a potential alternative to determine the urea concentration in DEF.

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.

Online quality control of panaxatriol saponins percolation extraction using near-infrared technology

Purpose : To establish a new prediction model for online quality control of the percolation extraction of panaxatriol saponins (PTS), viz, ginsenoside Rg1, ginsenoside Re and notoginsenoside R1, from notoginseng by near-infrared (NIR) technology coupled with partial least squares (PLS) analysis. Methods : Ten batches of PTS (420 samples) were collected and the constituents were determined using HPLC. The NIR spectroscopy of samples was determined using a Fourier-Transform nearinfrared spectrometer with an optical fiber transmission PbS detector. Eight sample batches were the calibration set, and two batches were the forecast set. Calibration models were established based on min-max normalization (MMN). Results : The root mean square errors of cross-validation (RMSECV) of Rg1, Re, and R1 were 0.798, 0.095, and 0.259 mg/mL, respectively. The root mean square errors of prediction (RMSEP) were 1.110, 0.496, and 0.390 mg/mL, respectively. The correlation coefficients (R2) of cross-validation were 0.9682, 0.9681, and 0.9626, respectively, while the correlation coefficients (R2) of prediction were 0.9831, 0.9198, and 0.9661, respectively. Conclusion : The results indicate that NIR is a quick and effective tool for online quality control of PTS (ginsenoside Rg1, ginsenoside Re, and notoginsenoside R1) in the percolation extraction process. Keywords : Online monitoring, Near infrared technology, Panaxatriol saponins, Partial least squares

Handling of uncertainty due to interference fringe in FT-NIR transmittance spectroscopy — Performance comparison of interference elimination techniques using glucose-water system

The applicability of two elimination techniques for interferences occurring in measurements with cells of short pathlength using Fourier transform near-infrared (FT-NIR) spectroscopy was evaluated. Due to the growing interest in the field of vibrational spectroscopy in aqueous biological fluids (e.g. glucose in blood), aqueous solutions of d-(+)-glucose were prepared and split into a calibration set and an independent validation set. All samples were measured with two FT-NIR spectrometers at various spectral resolutions. Moving average smoothing (MAS) and fast Fourier transform filter (FFT filter) were applied to the interference affected FT-NIR spectra in order to eliminate the interference pattern. After data pre-treatment, partial least squares regression (PLSR) models using different NIR regions were constructed using untreated (interference affected) spectra and spectra treated with MAS and FFT filter. The prediction of the independent validation set revealed information about the performance of the utilized interference elimination techniques, as well as the different NIR regions. The results showed that the combination band of water at approx. 5200 cm−1 is of great importance since its performance was superior to the one of the so-called first overtone of water at approx. 6800 cm−1. Furthermore, this work demonstrated that MAS and FFT filter are fast and easy-to-use techniques for the elimination of interference fringes in FT-NIR transmittance spectroscopy.

Rapid evaluation of the salt content of canned sardines in brine using near-infrared diffuse reflectance spectroscopy

ABSTRACTIn this study, the evaluation of the salt content of canned sardines in brine using near-infrared (NIR) spectroscopy with a Fourier transform NIR spectrometer in diffuse reflectance mode over a range of 12,500–4000 cm−1 was investigated. The reference salt content of the samples was measured using an autotitrator. The optimum partial least squares regression model was developed using vector normalization spectra in the ranges of 9403.8–5446.3 cm−1 and 4605.4–4242.9 cm−1 with seven factors. The model provided a coefficient of determination for a prediction of 0.82, a root mean square error of prediction of 0.069%, a bias of −0.019%, and a ratio of prediction deviation of 2.5. NIR spectroscopy has great potential as an alternative method for measuring the salt content of canned sardines in brine, and the calibration model is useful for the off-line quality control of canned sardines in brine processing. In addition, the oxygen–hydrogen bond stretch and deformation and the carbon–hydrogen bond stretch and deformation had obvious effects on the prediction of the salt content of canned sardines in brine.

Quality Estimation of Agave Tequilana Leaf for Bioethanol Production

Agave tequilana is a potential biofuel crop, for which the characters of juice total soluble sugar content (TSS), dry matter content (DM), cellulose, hemicellulose and lignin content are quality criteria. Spectra of leaves were obtained using a hand-held silicon photodiode array (Si PDA)-based spectrometer with a wavelength range of 300–1100 nm and an InGaAs-based Fourier transform near infrared (FT-NIR) spectrometer with a wavelength range of 1100–2500 nm. Fresh leaves were harvested at different maturity stages, in different seasons and from two locations in Queensland during 2012–2014. Partial least square regression models were developed for DM and TSS of fresh leaf, and for cellulose, hemicellulose and lignin of dried material, with models tested on populations of independent samples collected in different years, seasons and locations. Prediction statistics for DM of fresh leaf using the Si PDA spectrometer (729–975 nm) were r2 = 0.49–0.87 and root mean square error of prediction ( RMSEP) = 2.36–1.44%, while with the use of the FT-NIR spectrometer, the prediction statistics were r2 = 0.53–0.66 and RMSEP = 2.63–2.18% (across different years, seasons and locations). Prediction statistics for TSS in fresh leaf using the Si PDA spectrometer (729–975 nm) were r2 = 0.53–0.69 and RMSEP = 1.70–1.91%, with poorer results obtained using the FT-NIR spectrometer ( r2 = 0.33–0.56; RMSEP = 1.88–2.45%). With increased sample diversity in the calibration set, NIR technology is recommended for estimation of DM and TSS in fresh Agave leaves. FT-NIR-based prediction of cellulose, hemicellulose or lignin of independent sets (of different years or cultivars) was unsatisfactory, with r2 &lt; 0.75 and bias &gt;10% of mean. These results may be improved with increased sample range, and attention to laboratory (reference method) error. However, leaf cellulose and hemicellulose content may be more easily estimated through correlation to leaf DM level ( R2 of 0.77 across all sampling events).