Near-infrared spectroscopy (NIRS) reflectance technology for the determination of tocopherols in alfalfa

The vitamin E (α- and (β+γ)-tocopherol) contents present in alfalfa (fresh or dehydrated) were analysed using near-infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe. The range of vitamin E was 0.55–5.16 mg/100 g for α-tocopherol and 0.07–0.48 for (β+γ)-tocopherol. The regression method employed was modified partial least squares (MPLS). The equations developed using the fibre-optic probe for 69 samples of alfalfa (dehydrated and fresh) to determine the content of vitamin E in feeds had multiple correlation coefficients (RSQs) and prediction corrected standard errors (SEP (C)) of 0.946 and 0.321 mg/100 g for α-tocopherol and 0.956 and 0.022 mg/100 g for (β+γ)-tocopherol. The predicted values of vitamin E in feeds using NIRS technology applying the fibre-optic probe directly on the sample with neither previous treatment nor manipulation are comparable to those obtained using the chemical method, which included alkaline hydrolysis and hexane extraction of the vitamin from the unsaponifiable fraction before chromatographic determination.     

Use of NIRS technology with a remote reflectance fibre-optic probe for predicting major components in cheese

In the present work the potential of near infra-red spectroscopy technology (NIRS) together with the use of a remote reflectance fibre-optic probe for the analysis of fat, moisture, protein and chlorides contents of commercial cheeses elaborated with mixtures of cow's, ewe's and goat's milk and with different curing times was examined. The probe was applied directly, with no previous sample treatment. The regression method employed was modified partial least squares (MPLS). The equations developed for the cheese samples afforded fat, moisture, protein, and chloride contents in the range 13-52%, 10-62%, 20-30%, and 0.7-2.9%, respectively. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP (C)) obtained were respectively 0.97 and 0.995% for fat; 0.96% and 1.640% for moisture; 0.78% and 0.760% for protein, and 0.89% and 0.112% for chlorides.     

Use of NIRS technology with a remote reflectance fibre-optic probe for predicting major components in bee pollen

In the present work, we study the use of near infra-red spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for determination of the major components in bee pollen. The method allows immediate control of the bee pollen without prior sample treatment or destruction through direct application of the fibre-optic probe to the sample.The regression method employed was modified partial least squares (MPLS). The calibration results obtained using 45 samples of bee pollen allowed the measurement of protein, moisture, ash, reducing sugars, and pH with multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEPC) of 0.91, 0.56% for protein, of 0.78 and 0.49% for moisture; 0.92 and 0.049% for ash; 0.81 and 1.32 g of glucose/100 g of bee pollen; 0.84 and 0.15 for pH, respectively.The prediction capacity of the pattern was checked by applying it to samples of unknown pollen in external validation.     

Determination of the percentage of milk (cow's, ewe's and goat's) in cheeses with different ripening times using near infrared spectroscopy technology and a remote reflectance fibre-optic probe

In the present work we studied the use of near infrared spectroscopy (NIRS) technology employing a remote reflectance fibre-optic probe (with a 5 cm × 5 cm quartz window) for the analysis of the percentage of milk (cow's, ewe's and goat's) used in the elaboration of cheeses with different ripening times. To do so, cheeses with known and varying percentages of cow's, ewe's and goat's milk were elaborated (112 samples with milk collected in winter and 112 samples with milk collected in summer) and used as reference material, and ripening controls were performed over 6 months. The method allows immediate control of the cheese without prior sample treatment or destruction by direct application of the fibre-optic probe to the sample. The regression method employed was modified partial least squares (MPLS). Of all the samples (224), 200 formed to so-called calibration set and the other 24 were used for external validation. The calibration results obtained using 200 samples of cheese allowed the percentage of cow's, ewe's and goat's milk to be measured. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP(C)) obtained were respectively, 0.834 and 11.6% for cow's milk; 0.871 and 9.8% for goat's milk; 0.880 and 10.6% for ewe's milk. The ratio performance deviation (RPD) values obtained indicate that the NIRS equations can be applied to unknown samples.     

Use of NIRS technology with a remote reflectance fibre-optic probe for predicting mineral composition (Ca,K, P,Fe, Mn,Na, Zn), protein and moisture in alfalfa

In the present work we study the use of near-infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for the analysis of major (Ca, K, P) and minor (Fe, Mn, Na, Zn) elements, protein and moisture in alfalfa. The method allows immediate analysis of the alfalfa without prior sample treatment or destruction through direct application of the fibre-optic probe on ground samples in the case of the mineral composition and on-ground and compacted (baled) samples in the case of protein and humidity. The regression method employed was modified partial least-squares (MPLS). The calibration results obtained using samples of alfalfa allowed the determination of Ca, K, P, Fe, Mn, Na and Zn, with a standard error of prediction (SEP(C)) and a correlation coefficient (RSQ) expressed in mg/kg of alfalfa of 1.37 × 10³ and 0.878 for Ca, 1.10 × 10³ and 0.899 for K, 227 and 0.909 for P, 103 and 0.948 for Fe, 5.1 and 0.843 for Mn, 86.2 and 0.979 for Na, and of 1.9 and 0.853 for Zn, respectively. The SEP(C) and RSQ values (in %) for protein and moisture in ground samples were 0.548 and 0.871 and 0.150 and 0.981, respectively; while in the compacted samples they were 0.564 and 0.826 and 0.262 and 0.935, respectively. The prediction capacity of the model and the robustness of the method were checked in the external validation in alfalfa samples of unknown composition, and the results confirmed the suitability of the method.     

Determination of inorganic elements in animal feeds by NIRS technology and a fibre-optic probe

In the present work we study the use of near infra-red spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for the analysis of the mineral composition of animal feeds. The method allows immediate control of the feeds without prior sample treatment or destruction through direct application of the fibre-optic probe on the sample.The regression method employed was modified partial least squares (MPLS). The calibration results obtained using forty samples of animal feeds allowed the determination of Fe, Mn, Ca, Na, K, P, Zn and Cu, with a standard error of prediction (SEP(C)) and a correlation coefficient (RSQ) of 0.129 and 0.859 for Fe; 0.175 and 0.816 for Mn; 5.470 and 0.927 for Ca; 2.717 and 0.862 for Na; 4.397 and 0.891 for K; 2.226 and 0.881 for P; 0.153 and 0.764 for Zn, and 0.095 and 0.918 for Cu, respectively.The robustness of the method was checked by applying it to 10 animal feeds samples of unknown mineral composition in the external validation.     

Potential of near infrared spectroscopy for the analysis of mycotoxins applied to naturally contaminated red paprika found in the Spanish market

The potential of the near infrared spectroscopy (NIRS) technique for the analysis of red paprika for aflatoxin B(1), ochratoxin A and total aflatoxins is explored. As a reference, the results from a chromatographic method with fluorescence detection (HPLC-FD) following an immunoaffinity cleanup (IAC) were employed. For the NIRS measurement, a remote reflectance fibre-optic probe was applied directly onto the samples of paprika. There was no need for pre-treatment or manipulation of the sample. The modified partial least squares (MPLS) algorithm was employed as a regression method. The multiple correlation coefficients (RSQ) and the prediction corrected standard errors (SEP(C)) were respectively 0.955 and 0.2 microg kg(-1), 0.853 and 2.3 microg kg(-1), 0.938 and 0.3 microg kg(-1) for aflatoxin B(1), ochratoxin A and total aflatoxins, respectively. The capacity for prediction of the developed model measured as ratio performance deviation (RPD) for aflatoxin B(1) (5.2), ochratoxin A (2.8) and total aflatoxins (4.4) indicate that NIRS technique using a fibre-optic probe offers an alternative for the determination of these three parameters in paprika, with an advantageously lower cost and higher speed as compared with the chemical method. Content of aflatoxin B(1) and total aflatoxins are the parameters currently employed by the food regulations to limit the levels of the four aflatoxins in many foodstuffs. In addition, aflatoxin B(1) itself is an excellent indicator for aflatoxins' contamination since it is always the most abundant and toxic.     

A self-polishing platinum ring voltammetric sensor and its application to complex media

A self-polishing voltammetric sensor was recently developed and has been applied to samples of urea, milk and sewage water. The polishing device continuously grinds a platinum ring electrode, offering a reproducible and clean electrode surface. Principal component analysis (PCA) and partial least squares (PLS) techniques were applied to interpret the data and to build prediction models. In an evaluation of samples with different urea concentrations, the grinding step allows for repeatable measurements, similar to those after electrochemical cleaning. Furthermore, for the determination of sewage water concentrations in drinking water and for the evaluation of different fat contents in milk samples, the polishing eliminates sensor drift produced by electrode fouling. The results show that the application of a self-polishing unit offers a promising tool for electrochemical studies of difficult analytes and complex media.     

Determination of Basic Nitrogen in Residues of Crude Oil Distillation Using ATR-FTIR and Chemometric Methods

A method for basic nitrogen determination in residues of crude oil distillation using infrared spectroscopy and chemometrics algorithms was developed. Interval partial least squares, synergy interval partial least squares, and backward interval partial least squares were evaluated for calibration model construction. The samples were divided into a calibration and prediction set containing 40 and 15 samples, respectively. The first derivative with a Savitzky-Golay filter and the mean centered data showed the best results and were used in all calibration models. The backward interval partial least squares algorithm with spectra divided in 60 intervals and combinations of 4 intervals (1407 to 1372; 1117 to 1082; 971 to 936; 914 to 879 cm^?1) showed the best root mean square error of prediction of 0.016 wt%. This calibration model displayed a suitable correlation coefficient between reference and predicted values.     

基于组合偏最小二乘的特征波段优选方法在氨、碱化处理玉米秸秆粗蛋白检测中的研究

该文构建了玉米秸秆粗蛋白定量分析模型,并对光谱特征波段选取方法进行探讨及验证。首先对107个样本进行预处理,剔除两个异常样本后采用DB2小波缺省阈值4层分解方式进行光谱重构,预处理后粗蛋白模型交互验证决定系数R2CV从0.788 9提高至0.920 8,采用间隔偏最小二乘(IPLS)及其改进型方法后向区间间隔偏最小二乘(BIPLS)、组合间隔偏最小二乘(SIPLS)进行特征波段选取,并对比主成分分析、竞争性自适应重加权采样法、相关系数法、遗传算法、移动窗口最小二乘等结果,发现基于IPLS及其改进型BIPLS、SIPLS均可有效、准确定位特征波段区间,其中采用SIPLS 30 波段间隔在10 128~10 398 cm-1与11 196~11 462 cm-1时具有最优模型,验证集相关系数(rp)为0.978 4,验正集决定系数(R2P)为0.957 2,验正集均方误差根(RMSEP)为0.221 1,相比于其他波段选取方法表现出较好的实时准确性,该方法可为玉米秸秆氨碱化最优条件判定提供重要的数据支撑。     

Chemometrics-assisted spectrophotometric methods for simultaneous determination and complexation study of Fe(III), Al(III) and V(V) with morin in micellar media

Evolutionary factor analysis (EFA) and rank annihilation factor analysis (RAFA) were applied to resolve the two-way equilibrium spectrophotometric data belonging to the complexes of Fe(III), Al(III) and V(V) with morin (3,5,7,20,40-penta hydroxy flavone) as chelating agent in triton X-100 micellar media. Then, partial least square regression combined with genetic algorithm for wavelength selection (GA-PLS) was used for simultaneous determination of the metal ions. The parameters controlling behavior of the system were investigated and optimum conditions were selected. The predictive abilities of partial least squares regression (PLS) and genetic algorithm-partial least squares regression (GA-PLS) were examined in simultaneous determination of ternary mixtures of metal ions over the concentration range of 17.0-170.0ngml(-1), 25.0-180.0ngml(-1) and 40.0-325.0ngml(-1) for Fe(III), Al(III) and V(V), respectively. The relative standard errors for prediction of the ions in synthetic mixtures were lower than 5% and the mean recoveries in the tap water spiked samples were 104.2 and 101.7% for PLS and GA-PLS, respectively.     

NIR spectroscopic measurement of moisture content in Scots pine seeds

When tree seeds are used for seedling production it is important that they are of high quality in order to be viable. One of the factors influencing viability is moisture content and an ideal quality control system should be able to measure this factor quickly for each seed. Seed moisture content within the range 3-34% was determined by near-infrared (NIR) spectroscopy on Scots pine (Pinus sylvestris L.) single seeds and on bulk seed samples consisting of 40-50 seeds. The models for predicting water content from the spectra were made by partial least squares (PLS) and ordinary least squares (OLS) regression. Different conditions were simulated involving both using less wavelengths and going from samples to single seeds. Reflectance and transmission measurements were used. Different spectral pretreatment methods were tested on the spectra. Including bias, the lowest prediction errors for PLS models based on reflectance within 780-2280 nm from bulk samples and single seeds were 0.8% and 1.9%, respectively. Reduction of the single seed reflectance spectrum to 850-1048 nm gave higher biases and prediction errors in the test set. In transmission (850-1048 nm) the prediction error was 2.7% for single seeds. OLS models based on simulated 4-sensor single seed system consisting of optical filters with Gaussian transmission indicated more than 3.4% error in prediction. A practical F-test based on test sets to differentiate models is introduced.     

Development and validation of methods for the determination of miokamycin in various pharmaceutical preparations by use of near infrared reflectance spectroscopy

New methods for the determination of the nominal content of miokamycin in three commercial pharmaceutical preparations available in many different forms are proposed. Solid samples, grinding of which is the sole pretreatment required, are analysed by near infrared (NIR) spectroscopy, using a fibre-optic probe. The active principle is quantified by partial least-squares regression (PLSR). The three proposed methods were validated with a view to their use as control methods; the selectivity of the method, and the repeatability, intermediate precision, accuracy, linearity and robustness of each PLSR calibration model used were determined. The relative standard error of prediction (RSEP) was < 1.5% and the validation results testify to the suitability of the proposed methods.     

Quality assessment of gasoline using comprehensive two‐dimensional gas chromatography combined with unfolded partial least squares: A reliable approach for the detection of gasoline adulteration

Comprehensive two‐dimensional gas chromatography and flame ionization detection combined with unfolded‐partial least squares is proposed as a simple, fast and reliable method to assess the quality of gasoline and to detect its potential adulterants. The data for the calibration set are first baseline corrected using a two‐dimensional asymmetric least squares algorithm. The number of significant partial least squares components to build the model is determined using the minimum value of root‐mean square error of leave‐one out cross validation, which was 4. In this regard, blends of gasoline with kerosene, white spirit and paint thinner as frequently used adulterants are used to make calibration samples. Appropriate statistical parameters of regression coefficient of 0.996–0.998, root‐mean square error of prediction of 0.005–0.010 and relative error of prediction of 1.54–3.82% for the calibration set show the reliability of the developed method. In addition, the developed method is externally validated with three samples in validation set (with a relative error of prediction below 10.0%). Finally, to test the applicability of the proposed strategy for the analysis of real samples, five real gasoline samples collected from gas stations are used for this purpose and the gasoline proportions were in range of 70–85%. Also, the relative standard deviations were below 8.5% for different samples in the prediction set.     

A new method for simultaneous determination of 226Ra and uranium in aqueous samples by liquid scintillation using chemometrics

In this work, simultaneous determination of low levels of ²²⁶Ra and uranium in aqueous samples were performed by alpha-liquid scintillation counting (LSC) in conjunction with artificial neural network (ANN) and partial least squares (PLS). The counting rates at 73 channels, which were selected by genetic algorithm, were used for training. A PLS model with four latent variables and a principle component ANN model (4-4-2) with linear transfer function after hidden and output layers were created. Total relative error of prediction for PLS and ANN in synthetic mixtures was 18.05% and 24.78%, respectively. The matrix effect was studied by spiking the real samples with radium and uranium. Laser induced fluorescence was used for assessment of uranium prediction results in real samples.     

Selection of Wavelength Range and Number of Factors to be Used in the PLS Treatment of Spectrophotometric Data

Abstract

A procedure for selection of wavelength range and number of factors to be used in partial least square calibration that involves the calculation of prediction residual sum of squares (PRESS) in different conditions is proposed. The best model takes into account the minimum PRESS value that does not show significant differences with respect to the corresponding model with fewer factors. The ability of the proposed method to minimize errors in partial least squares (PLS) prediction is demonstrated by applying it to the resolution of phenytoine (DPH) and phenobarbital (PB) binary mixtures with errors less than 2.8%; the results are compared with those obtained using another wavelength selection procedure. The ensuing method, which was validated by high performance liquid chromatography (HPLC), also gives good results with real samples (pharmaceutical preparations).     

Analysis of oxonic acid, uric acid, creatine, allantoin, xanthine and hypoxanthine in poultry litter by reverse phase HPLC

Summary A separation method has been developed to extract organic compounds from poultry manure and litter and subsequently analyze these extracts using reverse phase high-pressure liquid chromatography. Specifically, the method may be used to quantify oxonic acid, allantoin, creatine, uric acid, xanthine and hypoxanthine in poultry manure samples. In a representative sample of fresh poultry manure, oxonic acid, allantoin, creatine, uric acid and xanthine were present at concentrations of 2.4, 2.7, 3.9, 270 and 0.8 mg/g dry manure, respectively. Litter samples stored for eight or sixteen weeks showed the presence of hypoxanthine but no longer contained detectable quantities of creatine. Allantoic acid and urea, both previously shown to be present in the litter samples, could not be distinguished by the chromatographic technique. Orotic acid and uracil, although capable of being separated by the method, were not detected in any poultry litter samples.     

Characterization of the Release of Urea from Coated Fertilizer by Fourier Transform Infrared Spectroscopy with Attenuated Total Reflectance

The development of a simple and cost-effective method for the determination of the release of coated urea has significant implications. Fourier-transform infrared spectroscopy with attenuated total reflectance was employed to determine the release of urea through univariate and multivariate calibration. The results indicated that univariate calibration did not accurately predict the release of urea, whereas partial least squares based on multivariate calibration performed significantly better. Partial least squares had the highest accuracy when the band located at 1420–1520 per centimeter was employed as the input. Moreover, the accuracy was further improved when segmented partial least squares models were developed at low and high urea concentrations. Unsegmented and segmented partial least squares models were employed, and release values were comparable to those measured by colorimetry. This work demonstrated the use of infrared spectroscopy and partial least squares to characterize the release of coated urea.     

Quantitation of κ-, ι- and λ-carrageenans by mid-infrared spectroscopy and PLS regression

Carrageenans are natural products obtained from seaweeds which are used as additives in many industrial fields, mainly in the food industry (labelled as E407). The three most employed ones being the so-called κ-, ι- and λ-carrageenans. So far, their industrial characterisation is based on physical measurements, which exhibit a rather large uncertainty. The aim of this work is to develop a new analytical methodology for the quantitative determination of carrageenans in industrial mixtures employing FTIR and multivariate regression (partial least squares, PLS), avoiding complex sample pre-treatment steps and reducing the turnaround time. The methodology allows to handle liquid (dissolved) carrageenans at room temperature (to prevent degradation), and therefore, their straightforward IR measurement using thin films. The standard prediction errors for the different models selected range from 3.3 to 4.2%, which can be considered as excellent.