Determination of total volatile basic nitrogen (TVB-N) content and Warner–Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy

Total volatile basic nitrogen (TVB-N) content is one of important index of pork’s freshness, and Warner–Bratzler shear force (WBSF) is seen as the important index of pork’s tenderness. This paper attempted the feasibility to determine TVB-N content and WBSF in pork by Fourier transform near infrared (FT-NIR) spectroscopy. Synergy interval partial least square (SI-PLS) algorithm was performed to calibrate regression model. The number of PLS factors and the number of intervals were optimised simultaneously by cross-validation. The performance of the model was evaluated according to two correlation coefficients (R) in calibration and prediction sets. Experimental results showed that the correlations coefficients in the calibration set (R_c) and prediction set (R_p) were achieved as follows: R_c = 0.8398 and R_p = 0.8084 for TVB-N content model; R_c = 0.7533 and R_p = 0.7041 for WBSF model. The overall results demonstrated that NIR spectroscopy combined with SI-PLS could be utilised to determinate TVB-N content and WBSF in pork.     

基于偏最小二乘法的板栗近红外光谱分析模型的建立

目的:应用近红外光谱技术和化学计量学方法,建立板栗品质分析的近红外光谱模型。方法:采用傅里叶变换近红外光谱仪,采集样品的近红外漫反射光谱,再用传统理化分析方法测得样品的各项品质参数,采用偏最小二乘法(PLS)建立定标模型,内部交叉验证法对模型进行检验。结果:对板栗分别建立了水分、淀粉、硬度和糖度的PLS模型,4种PLS模型都非常理想,模型的相关系数均大于0.99。结论:采用近红外光谱法可以实现板栗品质指标的快速无损检测。     

Preliminary study on the use of near‐infrared reflectance spectroscopy to assess nitrogen content of undried wheat plants

Near‐infrared reflectance (NIR) spectroscopy combined with chemometrics was used to assess nitrogen (N) and dry matter content (DM) and chlorophyll in whole‐wheat plant (Triticum aestivum L). Whole‐wheat plant samples (n = 245) were analysed by reference method and by visible and NIR spectroscopy, in fresh (n = 182) and dry (n = 63) presentations, respectively. Calibration equations were developed using partial least squares (PLS) and validated using full cross‐validation (leave‐one‐out method). Coefficient of determination in calibration (R²_CAL) and the standard error of cross‐validation (SECV) for N content in fresh sample presentation, after second derivative, were 0.89 (SECV: 0.64%), 0.86 (SECV: 0.66%) and 0.82 (SECV: 0.74%) using the visible + NIR, NIR and visible wavelength regions, respectively. Dry sample presentation gave better R²_CAL and SECV for N compared with fresh presentation (R²_CAL > 0.90, SECV < 0.20%) using visible + NIR. The results demonstrated that NIR is a suitable method to assess N concentration in wheat plant using fresh samples (unground and undried). Copyright © 2006 Society of Chemical Industry     

FT-NIR spectroscopy for caffeine estimation in instant green tea powder and granules

The feasibility of measuring caffeine content in instant green tea and granules was investigated by Fourier Transform Near-Infrared (FT-NIR) spectroscopic technique. A calibration model was developed using pure caffeine standards of varying concentrations in the near-infrared region (4000-12000 cm⁻¹). The developed model was validated using test validation technique. FT-NIR spectroscopy with chemometrics, using the PLS-first derivative plus straight line subtraction method could predict the caffeine content in tea samples accurately up to an R² value greater than 0.98 and a standard error of prediction (SEP) value less than 2.0 with 6 factors in the prediction model. The developed model was applied to predict caffeine content in tea samples within 2-5 min. The developed procedure was further validated by recovery studies by comparing with UV spectroscopic method of caffeine determination.     

Rapid Determination of Spinal Cord Content in Ground Beef by Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy

ABSTRACT: The potential of attenuated total reflectance Fourier transform infrared spectroscopy to measure spinal cord in ground beef was investigated. Mid‐infrared spectra were obtained for ground beef adulterated with 0 to 100 ppm spinal cord. Partial least squares models were developed using 400 to 4000 cm^‐1. The model in the 400 to 4000 cm‐1 range showed a calibration of R_call= 0.94 (RMSE = 11.24) and validation of R_val= 0.87 (RMSE = 17.22). The P‐O‐C and N‐H stretches were found to correlate positively with spinal cord content. Fourier transform infrared spectroscopy has the potential to rapidly determine spinal cord adulteration in ground beef.     

Identification of pork in beef meatballs using Fourier transform infrared spectrophotometry and real-time polymerase chain reaction

A study on development of Fourier-transform infrared spectrophotometric method combined with principle component analysis as well as real-time polymerase chain reaction for determination of pork–beef mixture in meatballs has been performed. A lipid component extracted from pork and beef in meatballs is analyzed using Fourier-transform infrared spectroscopy, while DNA extracted from meatball was analyzed using real-time polymerase chain reaction. The correlation between actual and predicted concentration of lard using Fourier-transform infrared spectroscopy was performed by aid of partial least squares, while grouping of lard and beef fat components in meatball was carried out by Fourier-transform infrared spectra coupled with principle component analysis. The results showed that Fourier-transform infrared spectra at wavenumbers of 1000–1200 cm^?1 coupled with partial least square and principle component analysis are successfully used for quantification and classification of pork in beef meatballs. The relationship between actual value and predicted value of lard (lipid fraction obtained from meatballs containing pork) with Fourier-transform infrared spectrophotometric method revealed good correlation, with coefficient determination (R²) value of 0.997 and standard error of calibration of 0.04%. Principle component analysis is able to classify samples containing pork and beef meatballs. Fourier-transform infrared spectroscopy using normal spectra is fast technique for identification and quantification of lard extracted from pork in meatball. In addition, real-time polymerase chain reaction using Leptin Primer–AJ 865080 can be used for amplification of pork DNA specifically in meatballs containing pork.     

Near‐infrared reflectance spectroscopy for determining end‐point temperature of heated fish and shellfish meats

Attempts have been made to assess the end‐point temperature (EPT) of heated fish and shellfish meats by near‐infrared (NIR) reflectance spectroscopy. Since the presence of water affects NIR spectra, the influence of the water content of samples on the performance of NIR spectroscopy for determining EPT was also evaluated. Blue marlin (Makaira mazara), skipjack (Katsuwonus pelamis), red sea bream (Pagrus major), kuruma prawn (Penaeus japonicus) and scallop (Patinopecten yessoensis) meats were heat‐treated at different temperatures (5 °C intervals between 60 and 100 °C). NIR spectra were measured at 2 nm intervals between 1100 and 2500 nm. A stepwise multiple linear regression method was used to develop a calibration curve. Changes in NIR reflectance spectra upon heat treatment were related to the heating temperature. Moreover, the interference from the variation in water content on the prediction of EPT was eliminated by selecting appropriate wavelengths. Plotting of NIR‐predicted temperatures determined from d² log(1/R) at four specific wavelengths against the actual EPT revealed a promising linear relationship with correlation coefficients between 0.94 and 0.98. In the temperature range 60–100 °C, NIR reflectance spectroscopy was able to detect EPT of heated fish and shellfish meats with a prediction error of 1.9–3.1%. © 2002 Society of Chemical Industry     

Development of a rapid, non-destructive method for egg content determination in dry pasta using FT-NIR technique

A quick, non-destructive method, based on Fourier transform near-infrared (FT-NIR) spectroscopy for egg content determination of dry pasta is presented. Multivariate calibration was carried out by using partial least squares (PLS) regression. A calibration set of 108 samples, a validation set of 22 samples and a prediction set of 11 samples of egg pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 800-2500 nm spectral range. The optimal results for egg content (root mean square error of prediction (RMSEP) = 0.7; R² = 90.7, Rank = 4) were obtained when the spectra were subjected to the first derivation combined with multiplicative scatter correction (MSC) and smoothing. Egg content was determined from FT-NIR results by introducing a mathematical correction step.     

On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy

Visible/near-infrared (Vis/NIR) spectroscopy was tested to predict the quality attributes of fresh pork (content of intramuscular fat, protein and water, pH and shear force value) on-line. Vis/NIR spectra (350–1100 nm) were obtained from 211 samples using a prototype. Partial least-squares regression (PLSR) models were developed by external validation with wavelet de-noising and several pre-processing methods. The 6th order Daubechies wavelet with 6 decomposition levels (db6–6) showed high de-noising ability with good information preservation. The first derivative of db6–6 de-noised spectra combined with multiplicative scatter correction yielded the prediction models with the highest coefficient of determination (R²) for all traits in both calibration and validation periods, which were all above 0.757 except for the prediction of shear force value. The results indicate that Vis/NIR spectroscopy is a promising technique to roughly predict the quality attributes of intact fresh pork on-line.     

Nondestructive determination of soluble solids content and pH in tomato juice using NIR transmittance spectroscopy

The potential of near-infrared (NIR) transmittance spectroscopy to nondestructively detect soluble solids content (SSC) and pH in tomato juices was investigated. A total of 200 tomato juice samples were used for NIR spectroscopy analysis at 800–2400 nm using an FT-NIR spectrometer. Multiplicative signal correction (MSC), and the first and second derivative were applied for pre-processing spectral data. The relationship between SSC, pH, and FT-NIR spectra of tomato juice were analyzed via partial least-squares (PLS) regression. PLS regression models were able to predict SSC and pH in tomato juices. The r _c, RMSEC, RMSEP, and RMSECV for SSC were 0.92, 0.0703°Brix, 0.150°Brix, and 0.138°Brix, respectively, whereas those values for pH were 0.90, 0.0333, 0.0316, and 0.0489, respectively. It is concluded that the combination of NIR transmittance spectroscopy and PLS methods can be used to provide a technique of convenient, versatile, and rapid analysis for SSC and pH in tomato juices.     

莲藕成分的近红外光谱分析模型的建立

目的:应用近红外光谱技术和化学计量学方法直接测定莲藕的常规指标。方法:用傅里叶变换近红外光谱仪采集样品的近红外漫反射光谱,再用传统理化分析方法测得样品的各品质参数;采用偏最小二乘(PLS)法建立定标模型,并采用内部交叉验证法对模型进行检验。结果:分别建立了莲藕水分、粗纤维、质构和糖度的PLS模型,其中质构的PLS模型最理想,模型的相关系数大于0.97;莲藕粗纤维、糖度和水分的PLS模型的相关系数均大于0.88。结论:采用近红外光谱法可以实现莲藕品质指标的快速无损检测。     

Rapid Determination of Spinal Cord Content in Ground Beef by Near-Infrared Spectroscopy

The potential of near‐infrared spectroscopy to measure spinal cord content in ground beef was investigated. Near‐infrared spectra were obtained for ground beef adulterated with 0 to 100 ppm spinal cord. A partial least square model was developed with 2nd derivative near‐infrared spectral data (5400/cm to 10000/cm). The model showed a calibration of R_val= 0.94 and a validation of R_val= 0.90. The detection limit of the model was 21 ppm, and sensitivity to detect true positive spinal cord–adulterated samples was 87%. Near‐infrared spectroscopy has the potential to identify spinal cord–adulterated ground beef.     

Value Adding to Red Grape Pomace Exploiting Eco-friendly FT-NIR Spectroscopy Technique

Agro-food industries require sustainable and profitable alternatives to disposal of their by-products. Grape pomace is a winemaking residue that represents a low-cost natural source of phenolic compounds with recognized antioxidant properties. In this work, Fourier-transform near-infrared (FT-NIR) spectroscopy and chemometric analysis were exploited to the characterization of red grape pomace composition (content of seeds and skins) and chemical properties as total phenolic content (TPC) and total antioxidant capacity (TAC). Raw (n?=?96) and milled (n?=?96) samples were evaluated by NIR spectroscopy and by classical batchwise assays, Folin-Ciocalteu, and ABTS for TPC and TAC, respectively, after different storage times (1 week to 2 months). Grape seeds had higher levels of TPC and TAC per sample dry weight when compared to grape skins. FT-NIR spectra of raw and milled samples were calibrated against content (%) of skins and seeds, TPC, and TAC using partial least squares (PLS) modeling. Spectral wavelength selection and latent variables were optimized for the lowest root-mean-square errors. PLS models’ results showed higher linearity for milled samples (0.936?>?R ²?>?0.914) when compared to raw samples (0.885?>?R ²?>?0.928). The range error ratio (RER) was between 10 and 14 for raw samples, while for milled grape pomace, it ranged from 15 to 18. Results confirmed that NIR spectroscopy can be applied to winemaking residues with virtually no sample processing needed to estimate the content of grape seeds and skins, the total phenolics, and total antioxidant capacity. Therefore, FT-NIR technique represents a non-destructive and eco-friendly technique to foster added value of grape pomace residues before time-consuming extraction steps are performed.     

A feasibility study on quantitative analysis of glucose and fructose in lotus root powder by FT-NIR spectroscopy and chemometrics

The feasibility of rapid analysis of glucose and fructose in lotus root powder by Fourier transform near-infrared (FT-NIR) spectroscopy was studied. Diffuse reflectance spectra were collected between 4000 and 12,432 cm⁻¹. Calibration models established by partial least-squares regression (PLSR), interval PLS of forward (FiPLS) and backward (BiPLS), back propagation-artificial neural networks (BP-ANN) and least squares-support vector machine (LS-SVM) were compared. The optimal models for glucose and fructose were obtained by LS-SVM with the first 10 latent variables (LVs) as input. For fructose the correlation coefficients of calibration (r_c) and prediction (r_p), the root-mean-square errors of calibration (RMSEC) and prediction (RMSEP), and the residual predictive deviation (RPD) were 0.9827, 0.9765, 0.107%, 0.115% and 4.599, respectively. For glucose the indexes were 0.9243, 0.8286, 0.543%, 0.812% and 1.785. The results indicate that NIR spectroscopy technique with LS-SVM offers effective quantitative capability for glucose and fructose in lotus root powder.     

Study of Fourier transform near infrared (FT‐NIR) spectra of ghee (anhydrous milk fat)

Ghee is chemically highly complex in nature. The authentication and characterisation of edible fats and oils by routine chemical methods are highly laborious and time‐consuming. Fourier transform near infrared (FT‐NIR) spectroscopy has emerged as the predominant analytical tool in the study of edible fats/oils. In order to assign absorption bands in the infrared (IR) spectrum, spectra of cow and buffalo ghee samples were acquired in the NIR region (10 000–4000 cm^?1). In the FT‐NIR spectrum, a total of nine peaks were obtained for cow and buffalo ghee, with almost equal intensity of absorption. The intensity of absorbance was higher for cow ghee compared to buffalo ghee.     

A rapid spectroscopic method for quantification of P57 in Hoodia gordonii raw material

The feasibility of quantifying the perceived active ingredient (P57) in Hoodia gordonii raw material using Fourier transform near- and mid-infrared spectroscopy combined with chemometric techniques was investigated. The concentration of P57 (a triterpene glycoside) was determined in 146 plant samples with liquid chromatography coupled to mass spectrometry and these values were used to develop a calibration model based on the partial least squares projections to latent structures (PLS) and orthogonal projections to latent structures (O-PLS) regression algorithms. The performance of each calibration model was evaluated according to the root mean square error of prediction (RMSEP) and correlation coefficient (R²). The PLS model with 2nd derivative pre-processing predicted P57 content based on the FT-NIR spectra with the best accuracy and a correlation coefficient (R²) value of 0.9629 and the lowest RMSEP of 0.03%. These results demonstrated that FT-NIR spectroscopy can be used to rapidly quantify P57 in H. gordonii raw material with high accuracy.     

近红外光谱技术在南极磷虾粉水分、脂肪和蛋白质含量快速检测中的应用

利用傅里叶近红外光谱分析技术,以磷虾粉样品的实测值与模型预测值为基础,研究了采用最小二乘法建立磷虾粉原始样品与磷虾粉混合样品中水分、脂肪和蛋白含量近红外定标模型的可行性和准确性.结果表明,磷虾粉近红外图谱最佳预处理方式为:标准正态变换预处理+一阶导数+Norris导数滤波;以磷虾粉混合样品构建的近红外模型较磷虾粉原始样...     

Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy

The use of fibre optic diffuse reflectance near infrared spectroscopy (NIR) in combination with chemometric techniques has been investigated to discriminate authenticity of honey. NIR spectra of unadulterated honey and adulterated honey samples with high fructose corn syrup were registered within 10,000–4000 cm⁻¹ spectral region. Discriminant partial least squares (DPLS) models were constructed to distinguish between unadulterated honey and adulterated honey samples and main bands responsible for the discrimination of samples are in the range of 6000–10,000 cm⁻¹. For these models, the correct classification rate for calibration samples were above 90%. Hundred percentage of unadulterated honey and 95% of adulterated honey samples from test set were correctly classified after appropriate preprocessing of first derivative, 13 smoothing points, followed by mean centering pre-treatment and eight model factors, respectively. Our results showed that NIR spectroscopy data with chemometrics techniques can be applied to rapid detecting honey adulteration with high fructose corn syrup.     

Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR

Camellia oil is often the target for adulteration or mislabeling in China because of it is a high priced product with high nutritional and medical values. In this study, the use of attenuated total reflectance infrared spectroscopy (MIR-ATR) and fiber optic diffuse reflectance near infrared spectroscopy (FODR-NIR) as rapid and cost-efficient classification and quantification techniques for the authentication of camellia oils have been preliminarily investigated. MIR spectra in the range of 4000–650 cm⁻¹ and NIR spectra in the range of 10,000–4000 cm⁻¹ were recorded for pure camellia oils and camellia oil samples adulterated with varying concentrations of soybean oil (5–25% adulterations in the weight of camellia oil). Identifications is successfully made base on the slightly difference in raw spectra in the MIR ranges of 1132–885 cm⁻¹ and NIR ranges of 6200–5400 cm⁻¹ between the pure camellia oil and those adulterated with soybean oil with soft independent modeling of class analogy (SIMCA) pattern recognition technique. Such differences reflect the compositional difference between the two oils with oleic acid being the main ingredient in camellia oil and linoleic acid in the soybean oil. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using first derivative by combination of standard normal variate (SNV), variance scaling (VS), mean centering (MC) and Norris derivative (ND) smoothing pretreatments yielded the best prediction results With MIR techniques. The R value for PLS model is 0.994.The root mean standard error of the calibration set (RMSEC) is 0.645, the root mean standard error of prediction set (RMSEP) and the root mean standard error of cross validation (RMSECV) are 0.667 and 0.85, respectively. While with NIR techniques, NIR data without derivative gave the best quantification results. The R value for NIR PLS model is 0.992. The RMSEC, RMSEP and RMSECV are 0.70, 1.78 and 1.79, respectively. Overall, either of the spectral method is easy to perform and expedient, avoiding problems associated with sample handling and pretreatment than the conventional technique.