The Ability of Near Infrared (NIR) Spectroscopy to Predict Functional Properties in Foods: Challenges and Opportunities

Near infrared (NIR) spectroscopy is considered one of the main routine analytical methods used by the food industry. This technique is utilised to determine proximate chemical compositions (e.g., protein, dry matter, fat and fibre) of a wide range of food ingredients and products. Novel algorithms and new instrumentation are allowing the development of new applications of NIR spectroscopy in the field of food science and technology. Specifically, several studies have reported the use of NIR spectroscopy to evaluate or measure functional properties in both food ingredients and products in addition to their chemical composition. This mini-review highlights and discussed the applications, challenges and opportunities that NIR spectroscopy offers to target the quantification and measurement of food functionality in dairy and cereals.     

Discrimination of industrial products by on-line near infrared spectroscopy with an improved dendrogram

Near infrared(NIR) spectroscopy technique has shown great power and gained wide acceptance for analyzing complicated samples.The present work is to distinguish different brands of tobacco products by using on-line NIR spectroscopy and pattern recognition techniques.Moreover,since each brand contains a large number of samples,an improved dendrogram was proposed to show the classification of different brands.The results suggest that NIR spectroscopy combined with principal component analysis (PCA) and hierarchical cluster analysis(HCA) performs well in discrimination of the different brands,and the improved dendrogram could provide more information about the difference of the brands.     

Rapid Determination of Cotton Content in Textiles by Near-Infrared Spectroscopy and Interval Partial Least Squares

Textile products must be marked by fabric type and composition on the label and cotton is by far the most important fiber in the industry and often needs fast quantitative analysis. The corresponding standard methods are very time-consuming and labor-intensive. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and interval-based partial least squares (iPLS) for determining cotton content in textiles. Three types of partial least square (PLS)-based algorithms were used for experimental measurements. A total of 91 cloth samples with cotton content ranging from 0 to 100% (w/w) were collected and all compositions are commercially available on the market in China. In all cases, the original spectrum axis was split into 20 subintervals. As a result, three final models, i.e., the iPLS model on a single subinterval, the backward interval partial least squares (biPLS) model on the region remaining six subintervals, and the moving window partial least squares (mwPLS) model with a window of 75 variables, achieved better results than the full-spectrum PLS model. Also, no obvious differences in performance were observed for the three models. Thus, either iPLS or mwPLS was preferred considering their simplicity, which suggested that iPLS and mwPLS combined with NIR technique may have potential for the rapid determination of the cotton content of textile products with comparable accuracy to standard procedures. In addition, this approach may have commercial and regulatory advantages that avoid labor-intensive and time-consuming chemical analysis.     

Improvement of the partial least squares model performance for oral glucose intake experiments by inside mean centering and inside multiplicative signal correction.

Near infrared (NIR) spectroscopy has become a promising technique for the in vivo monitoring of glucose. Several capillary-rich locations in the body, such as the tongue, forearm, and finger, have been used to collect the in vivo spectra of blood glucose. For such an in vivo determination of blood glucose, collected NIR spectra often show some dependence on the measurement conditions and human body features at the location on which a probe touches. If NIR spectra collected for different oral glucose intake experiments, in which the skin of different patients and the measurement conditions may be quite different, are directly used, partial least squares (PLS) models built by using them would often show a large prediction error because of the differences in the skin of patients and the measurement conditions. In the present study, the NIR spectra in the range of 1300-1900 nm were measured by conveniently touching an optical fiber probe on the forearm skin with a system that was developed for in vivo measurements in our previous work. The spectra were calibrated to resolve the problem derived from the difference of patient skin and the measurement conditions by two proposed methods, inside mean centering and inside multiplicative signal correction (MSC). These two methods are different from the normal mean centering and normal multiplicative signal correction (MSC) that are usually performed to spectra in the calibration set, while inside mean centering and inside MSC are performed to the spectra in every oral glucose intake experiment. With this procedure, spectral variations resulted from the measurement conditions, and human body features will be reduced significantly. More than 3000 NIR spectra were collected during 68 oral glucose intake experiments, and calibrated. The development of PLS calibration models using the spectra show that the prediction errors can be greatly reduced. This is a potential chemometric technique with simplicity, rapidity and efficiency in the pretreatment of NIR spectra collected during oral glucose intake experiments.     

Discrimination of ethylene/vinyl acetate copolymers with different composition and prediction of the content of vinyl acetate in the copolymers and their melting points by near-infrared spectroscopy and chemometrics

Near-infrared (NIR) diffuse reflectance spectra have been measured by use of a rotating drawer for pellets of 12 kinds of ethylene/vinyl acetate (EVA) copolymers with vinyl acetate (VA, the comonomer) varying in the 7–44 wt % range. They are unambiguously discriminated from one another by a score plot of the principal component analysis (PCA) Factor 1 and 2, based upon the NIR spectra pretreated by multiplicative scatter correction (MSC). Principal component (PC) weight loadings for Factor 1 show that the discrimination relies largely upon bands due to the overtone and combination modes arising from the VA unit. We have found one “outlier” in the score plot and elucidated its spectral characteristics based upon PC weight loadings for Factor 2. Partial least-squares (PLS) regression has been applied to propose calibration models which predict the VA content in EVA. The models have been prepared for three kinds of pretreatment, the first derivative, the second derivative, and MSC; and four kinds of wavelength regions. The NIR spectra in the 1100–2200 nm region after the MSC treatment has given the best correlation coefficient and standard error of prediction (SEP) of 0.998 and 0.70%, respectively. The calibration models, prepared by NIR diffuse reflectance spectroscopy for the pellet samples, are compared with previously reported models by NIR transmission spectroscopy for the flowing molten samples, and with those by Raman spectroscopy for the pellet samples. PLS regression has also allowed us to predict melting points of the copolymers with the correlation coefficient and SEP of 0.997 and 0.78°C, respectively. © 1998 John Wiley & Sons, Inc. J Polym Sci B: Polym Phys 36: 1529–1537, 1998     

Quantitative Determination of Asphaltenes and Resins in Solution by Means of Near-Infrared Spectroscopy. Correlations to Emulsion Stability

Near-infrared (NIR) spectroscopy in the range 1100-2250 nm together with a latent-variable regression technique is used to analyze the content of asphaltene and resins in solution. It is shown that this technique is capable of determining the amount of these components individually. w/o emulsions were prepared from the separated components of asphaltenes and resins from crude oils. The stability was directly determined with the critical voltage in a dielectric instrumentation. The emulsion stability decreased linearly with an increase in the resin/asphaltene ratio. A final linear model correlating the critical voltage and the analytical concentrations (from the NIR spectra) could be established for this model system. Copyright 2000 Academic Press.     

Classification of Brazilian Coffee Using Near-Infrared Spectroscopy and Multivariate Calibration

This work describes the use of near infrared spectroscopy (NIRS) and chemometric techniques calibration for the classification of coffee samples from different lots and producers acquired in supermarkets and roasting industries in some Brazilian cities. Seventy-three samples of finely ground roasted coffee were acquired in the market and 91 samples of roasted ground Arabica beans were analyzed in the full NIR spectral range (800–2500 nm) using a diffuse reflectance accessory coupled to an MB160 Bomem spectrophotometer. Two classification models were constructed: Soft Independent Modeling Class Analogy (SIMCA) and PLS Discriminant Analysis (PLS-DA). All findings reveal that NIR spectroscopy, coupled with either SIMCA or PLS-DA multivariate models, can be a useful tool to differentiate roasted coffee grains and to replace sensory tests.     

Effects of digestion,chemical separation,and deposition on Po-210 quantitative analysis

The purpose of this work was to determine polonium losses from a variety of sample types (soil, cotton fiber, and air filter) due to digestion technique, chemical separation, and deposition method for alpha energy analysis. Results demonstrated that yields from a perchloric acid wet-ash (87 ± 5 %) were similar to that from a microwave digestion (100 ± 7 %), but both were greater than the dry-ash procedure (38 ± 5 %). The polonium was separated from an SRM soil using an AG1X8 ion exchange column and deposited on a Ag disk with a recovery of 83 ± 7 % of polonium-209 (Po-209). Deposition yields without chemical separation averaged 90 ± 5 % of Po-209. The polonium-210 content was successfully measured in the three matrix types and quantitated using alpha spectroscopy.     

Classification of cultivation area of ginseng by near infrared spectroscopy and ICP-AES

A rapid and nondestructive near infrared (NIR) method using soft independent modeling of class analogy (SIMCA) for the classification of cultivation area (Korea and China) was evaluated and confirmed. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of ginseng samples were also investigated to find out the differences between Korean samples and Chinese samples. These differences make NIR spectroscopic method viable. The average value of each Korean and Chinese ginseng sample for crude fiber, crude protein, starch, and 10 inorganic constituents were measured and compared with F-test and t-test. The inorganic constituents were also measured by induced coupled plasma-atomic emission spectroscopy (ICP-AES). It could be found that the amount of starch and ten inorganic elements for example Na, Mg, P, K, Ca, Mn, Fe, Ni, Cu and Zn in ginseng samples are considerably different based on cultivation area. SIMCA has been applied to the inorganic data to investigate the possibility of ICP-AES as classification tool. However, it was observed that the result was not equal to than NIR spectra data. The overall results showed the availability of NIR method using SIMCA would be adequate for classification of cultivation of ginseng, since NIR spectra includes useful and various information on chemical properties in spite of broad and overlapped bands.     

Rapid determination of hydrogen peroxide in whitening patches for teeth using a new portable near-infrared spectrometer

A nondestructive determination method of hydrogen peroxide in whitening patches for teeth was developed by using a new portable near-infrared (NIR) spectrometer. Development of the portable NIR spectrometer was based on microchip technologies with photodiode arrays. By using the portable NIR spectrometer, the new determination method is very rapid; it requires less than 1 s. The conventional method for the determination of hydrogen peroxide, redox titration, requires about 2 h of analysis, including the sample extraction time from a sample matrix. The conventional method also uses hazardous and harmful solvents and, furthermore, its samples cannot be used after titration. To find the peak due to the O–H bond vibration of hydrogen peroxide under the existence of water which shows huge absorption O–H absorption around 1450 nm, the NIR spectra of a hydrogen peroxide aqueous solution were investigated. A clear variation of absorption based on the concentration of the hydrogen peroxide due to the O–H bond vibration was found in the standard deviation plot around 1400 nm. In this study, two kinds of whitening patch products, A and B, were used for samples. A partial least squares (PLS) regression was used for calibration and validation in the 1100 to 1720 nm spectral range. For validation results, the standard error of prediction (SEP) was 0.38% for Patch A and 0.37% for Patch B. This study shows the feasibility of using the portable NIR spectrometer with photodiode arrays for the rapid and safe determination of hydrogen peroxide in whitening patches.     

Application of two‐dimensional near‐infrared (2D‐NIR) correlation spectroscopy to the discrimination of three species of Dendrobium

The objective of this paper was to apply two‐dimensional (2D) near‐infrared (NIR) correlation spectroscopy to the discrimination of three species of Dendrobium. Generalized 2D‐NIR correlation spectroscopy was able to enhance spectral resolution, simplify the spectrum with overlapped bands and provide information about temperature‐induced spectral intensity variations that was hard to obtain from one‐dimensional NIR spectroscopy. The FT‐NIR spectra were measured over a temperature range of 30–140°C. The 2D synchronous and asynchronous spectra showed remarkable differences within the range of 5600–4750 cm⁻¹ between different species of Dendrobium. Copyright © 2009 John Wiley & Sons, Ltd.     

近红外光谱快速测定混胺组分含量

研究了用近红外光谱分析混胺组分三乙胺和二甲苯胺含量的方法。对波长范围的选择和基线处理方法进行了研究,用偏最小二乘法线性回归分析建立三乙胺和二甲苯胺的校正模型。将近红外法测定结果与标准方法测定结果进行了比较,对光谱测量的重复性进行了考察,对温度的影响进行了分析。所有分析研究结果表明：在试样恒温的条件下,近红外光谱分析简单、快速、准确。     

On-line monitoring of the acrylate conversion in UV photopolymerization by near-infrared reflection spectroscopy

Near-infrared (NIR) reflection spectroscopy was used to determine the conversion in acrylate coatings after UV photopolymerization in order to test it as a method for process control in UV curing. A probe head was developed which is adapted to the specific requirements of UV curing and which is linked to a photodiode array spectrometer by an optical fiber. Reflection spectra from thin acrylate layers which were taken in intervals down to the millisecond range have shown an excellent signal-to noise ratio. Quantitative conversion data show good correlation with results from independent reference methods (FTIR, HPLC). Following thesebasic investigations, it was demonstrated that NIR reflection spectroscopy can be used for on-line monitoring of the acrylate conversion in thin coatings. Some examples of such investigations in pilot scale are presented.     

An inexpensive, portable and microcontrolled near infrared LED-photometer for screening analysis of gasoline

A microcontrolled, portable and inexpensive photometer is proposed. It uses a near infrared light emitting diode (NIR LED) as radiation source, a PbSe photoresistor as infrared detector and a programmable interrupt controller (PIC) microcontroller as control unit. The detector system presents a thermoresistor and a thermoelectric cooling to control the detector temperature and keep the noise at low levels. The microcontroller incorporated total autonomy on the proposed photometer. As its components are inexpensive and of easy acquisition, the proposed NIR LED-photometer is an economical alternative for chemical analyses in small routine, research and/or teaching laboratories. By being portable and microcontrolled, it also allows carrying out field chemical analyses. The instrument was successfully applied on the screening analysis to verify adulteration in gasoline samples.     

Quantitative analysis of routine chemical constituents in tobacco by near-infrared spectroscopy and support vector machine

It is important to monitor quality of tobacco during the production of cigarette. Therefore, in order to scientifically control the tobacco raw material and guarantee the cigarette quality, fast and accurate determination routine chemical of constituents of tobacco, including the total sugar, reducing sugar, Nicotine, the total nitrogen and so on, is needed. In this study, 50 samples of tobacco from different cultivation areas were surveyed by near-infrared (NIR) spectroscopy, and the spectral differences provided enough quantitative analysis information for the tobacco. Partial least squares regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The quantitative analysis models of 50 tobacco samples were studied comparatively in this experiment using PLSR, ANN, radial basis function (RBF) SVM regression, and the parameters of the models were also discussed. The spectrum variables of 50 samples had been compressed through the wavelet transformation technology before the models were established. The best experimental results were obtained using the (RBF) SVM regression with gamma=1.5, 1.3, 0.9, and 0.1, separately corresponds to total sugar, reducing sugar, Nicotine, and total nitrogen, respectively. Finally, compared with the back propagation (BP-ANN) and PLSR approach, SVM algorithm showed its excellent generalization for quantitative analysis results, while the number of samples for establishing the model is smaller. The overall results show that NIR spectroscopy combined with SVM can be efficiently utilized for rapid and accurate analysis of routine chemical compositions in tobacco. Simultaneously, the research can serve as the technical support and the foundation of quantitative analysis of other NIR applications.     

Monitoring lipase-catalyzed butterfat interesterification with rapeseed oil by Fourier transform near-infrared spectroscopy

This work demonstrates the application of FT-NIR spectroscopy in order to monitor the enzymatic interesterification process for butterfat modification. The reactions were catalyzed by Lipozyme TL IM at 70 °C for the blend of butterfat/rapeseed oil (70/30, w/w) in a packed-bed reactor. The blend and interesterified fat samples were measured in liquid form at 70 °C by transmission mode-based FT-NIR over the spectral region 12000–4000 cm⁻¹. The calibration of FT-NIR for conversion degree (evaluated by the triglyceride profile, which was represented by the triglyceride peak ratio) and solid fat content (SFC) of the interesterified products was carried out using partial least squares (PLS) regression. Good correlations were observed between the NIR spectra and ln (peak ratio), and between the NIR spectra and the SFC at 5 °C over the spectral range 5269–4513 cm⁻¹. Overall, transmission-mode FT-NIR spectroscopy performed at 70 °C yielded conditions close to those used during the interesterification process, implying that this method could be used to control the enzymatic interesterification process online.     

Tensile deformation of isotactic polypropylene (iPP) and iPP-nanocomposite studied by rheo-optical near-infrared (NIR) spectroscopy

Tensile deformations of isotactic polypropylene (iPP) and its nanocomposite were examined by a rheo-optical characterization technique based on near-infrared (NIR) spectroscopy to derive the submolecular-level understanding of the deformation mechanism during a tensile test. Sets of NIR spectra of the iPP samples were collected by using an acousto-optic tunable filter (AOTF) NIR spectrometer coupled with a tensile testing machine as an excitation device. Mechanical deformation of the samples was readily captured as strain-dependent NIR spectra. However, the main feature of the NIR spectra was overwhelmed by the contribution from the baseline change due to the substantial decrease in the sample thickness and subsequent change in the NIR light scattering. The variation of the spectral feature suggests that the deformation of the iPP involves the elongation of the rubbery amorphous chains prior to the displacement of the crystalline lamellae, providing elastic and subsequent plastic deformations during the tensile testing. In addition, it is revealed that the nanoclay layers dispersed within the iPP matrix restrict the elongation of the amorphous chains. Such interaction makes iPP hard and brittle, so that it yields no obvious ductile fracture during the tensile deformation.     

Noncontact analysis of the fiber weight per unit area in prepreg by near-infrared spectroscopy

The fiber weight per unit area in prepreg is an important factor to ensure the quality of the composite products. Near-infrared spectroscopy (NIRS) technology together with a noncontact reflectance sources has been applied for quality analysis of the fiber weight per unit area. The range of the unit area fiber weight was 13.39–14.14 mg cm⁻². The regression method was employed by partial least squares (PLS) and principal components regression (PCR). The calibration model was developed by 55 samples to determine the fiber weight per unit area in prepreg. The determination coefficient (R²), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.82, 0.092, 0.099, respectively. The predicted values of the fiber weight per unit area in prepreg measured by NIRS technology were comparable to the values obtained by the reference method. For this technology, the noncontact reflectance sources focused directly on the sample with neither previous treatment nor manipulation. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. Besides, the prepreg could be analyzed one time within 20 s without sample destruction.     

Near infrared reflectance spectroscopy as a process signature in uranium oxides

Near-infrared (NIR) reflectance spectroscopy was examined as a potential tool for the determination of forensic signatures indicative of the chemical process history of uranium oxides. The ability to determine the process history of nuclear materials is a desired, but underdeveloped, area of technical nuclear forensics. Application of the NIR technique potentially offers a quick and non-destructive tool to serve this need; however, few data have been published on the compounds of interest. The viability of NIR was investigated through the analysis of a combination of laboratory-derived and real-world uranium precipitates and oxides. A set of reference uranium materials was synthesized in the laboratory using the commonly encountered aqueous precipitation reactions for uranium ore concentration and chemical separation processes (ammonia, hydrogen peroxide, sodium hydroxide, ammonium carbonate, and magnesia). NIR spectra were taken on a range of samples heat treated in air between 85 and 750 °C. X-ray diffraction patterns were also obtained to complement the NIR analysis with crystal phase information. Similar analyses were performed using a set of real-world samples, with process information obtained from the literature, to provide a comparison between materials synthesized in the laboratory and samples representative of industrial processes.