Discrimination of transgenic tomatoes based on visible/near-infrared spectra

Identification of Transgenic Agricultural Products and Foods Using NIR Spectroscopy and Hyperspectral Imaging: A Review

Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.

Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles. In this sense, we propose and test a new covering array feature selection (CAFS) algorithm coupled to the naïve Bayes classifier (NBC) to discriminate among Amazonian cacao nibs from six cacao clones. The CAFS wrapper approach looks for the wavebands that maximize the F₁-score, and then, are more relevant for classification. For this purpose, cacao pods of six varieties were collected, and their grains were extracted and processed (fermented, dried, roasted, and milled) to obtain cacao nibs. Then from each clone NIR spectral profiles in the range of 1100-2500 nm were extracted, and relevant wavebands were selected using the proposed CAFS algorithm. For comparison, two standard feature selection techniques were implemented the multi-cluster feature selection MCFS and the eigenvector centrality feature selection ECFS. Then, based on the different selected variables, three NBCs were built and compared among them through statistical metrics. The results showed that using the wavebands selected by CAFS, the NBC performed an average accuracy of 99.63%; being this superior to the 94.92% and 95.79% for ECFS and MCFS respectively. These results showed that the wavebands selected by the proposed CAFS algorithm allowed obtaining a better fit concerning other feature selection methods reported in the literature.

Vis-NIR Spectroscopy and Machine Learning Methods for the Discrimination of Transgenic Brassica napus L. and Their Hybrids with B. juncea

The rapid advancement of genetically modified (GM) technology over the years has raised concerns about the safety of GM crops and foods for human health and the environment. Gene flow from GM crops may be a threat to the environment. Therefore, it is critical to develop reliable, rapid, and low-cost technologies for detecting and monitoring the presence of GM crops and crop products. Here, we used visible near-infrared (Vis-NIR) spectroscopy to distinguish between GM and non-GM Brassica napus, B. juncea, and F1 hybrids (B. juncea X GM B. napus). The Vis-NIR spectra were preprocessed with different preprocessing methods, namely normalization, standard normal variate, and Savitzky–Golay. Both raw and preprocessed spectra were used in combination with eight different chemometric methods for the effective discrimination of GM and non-GM plants. The standard normal variate and support vector machine combination was determined to be the most accurate model in the discrimination of GM, non-GM, and hybrid plants among the many combinations (99.4%). The use of deep learning in combination with Savitzky–Golay resulted in 99.1% classification accuracy. According to the findings, it is concluded that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used to distinguish between GM and non-GM B. napus, B. juncea, and F1 hybrids.

Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods

In recent years, the rapid development of genetically modified (GM) technology has raised concerns about the safety of GM crops and foods for human health and the ecological environment. Gene flow from GM crops to other crops, especially in the Brassicaceae family, might pose a threat to the environment due to their weediness. Hence, finding reliable, quick, and low-cost methods to detect and monitor the presence of GM crops and crop products is important. In this study, we used visible near-infrared (Vis-NIR) spectroscopy for the effective discrimination of GM and non-GM Brassica napus, B. rapa, and F1 hybrids (B. rapa X GM B. napus). Initially, Vis-NIR spectra were collected from the plants, and the spectra were preprocessed. A combination of different preprocessing methods (four methods) and various modeling approaches (eight methods) was used for effective discrimination. Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy rate (100%). The use of a Convolutional Neural Network with Normalization resulted in 98.9%. The same higher accuracy was found in the use of Gradient Boosted Trees and Fast Large Margin approaches. Later, phenolic acid concentration among the different plants was assessed using GC-MS analysis. Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes. The results showed that handheld Vis-NIR spectroscopy combined with chemometric analyses could be used for the effective discrimination of GM and non-GM B. napus, B. rapa, and F1 hybrids. Biochemical composition analysis can also be combined with the Vis-NIR spectra for efficient discrimination.

An Overview of Near Infrared Spectroscopy and Its Applications in the Detection of Genetically Modified Organisms

Near-infrared spectroscopy (NIRS) has become a more popular approach for quantitative and qualitative analysis of feeds, foods and medicine in conjunction with an arsenal of chemometric tools. This was the foundation for the increased importance of NIRS in other fields, like genetics and transgenic monitoring. A considerable number of studies have utilized NIRS for the effective identification and discrimination of plants and foods, especially for the identification of genetically modified crops. Few previous reviews have elaborated on the applications of NIRS in agriculture and food, but there is no comprehensive review that compares the use of NIRS in the detection of genetically modified organisms (GMOs). This is particularly important because, in comparison to previous technologies such as PCR and ELISA, NIRS offers several advantages, such as speed (eliminating time-consuming procedures), non-destructive/non-invasive analysis, and is inexpensive in terms of cost and maintenance. More importantly, this technique has the potential to measure multiple quality components in GMOs with reliable accuracy. In this review, we brief about the fundamentals and versatile applications of NIRS for the effective identification of GMOs in the agricultural and food systems.

Assessment of Tomato Maturity in Different Layers by Spatially Resolved Spectroscopy

Tomato maturity is important to determine the fruit shelf life and eating quality. The objective of this research was to evaluate tomato maturity in different layers by using a newly developed spatially resolved spectroscopic system over the spectral region of 550-1650 nm. Thirty spatially resolved spectra were obtained for 600 tomatoes, 100 for each of the six maturity stages (i.e., green, breaker, turning, pink, light red, and red). Support vector machine discriminant analysis (SVMDA) models were first developed for each of individual spatially resolved (SR) spectra to compare the classification results of two sides. The mean spectra of two sides with the same source-detector distances were employed to determine the model performance of different layers. SR combination by averaging all the SR spectra was also subject to comparison with the classification model performance. The results showed large source-detector distances would be helpful for evaluating tomato maturity, and the mean_SR 15 obtained excellent classification results with the total classification accuracy of 98.3%. Moreover, the classification results were distinct for two sides of the probe, which demonstrated even if in the same source-detector distances, the classification results were influenced by the measurement location due to the heterogeneity for tomato. The mean of all SR spectra could only improve the classification results based on the first three mean_SR spectra, but could not obtain the accuracy as good as the following mean_SR spectra. This study demonstrated that spatially resolved spectroscopy has potential for assessing tomato maturity in different layers.

Preliminary study on discrimination of transgenic cotton seeds using terahertz time-domain spectroscopy

Presence of genetically modified (GM) organisms is considered to be controversial by legislation and public. It is very important to develop detection methods for early discriminations. Conventional gene detection methods, including protein detection (PCR, ELISA, and so on) and DNA detection (Southern blot, GC/MS, and so on), have the disadvantages of high costs, time-consuming, complex operations, and destructive of the samples. Terahertz spectroscopy (THz) is a brand-new radiation with many unique advantages. Most biological macromolecules have fingerprint characteristics in THz band from the current recognition. In this study, feasibility of identifying the transgenic cotton seeds from nontransgenic counterparts using THz spectroscopy method was investigated. The transgenic cotton seeds-Lumianyan No.28 and nontransgenic cotton seeds-Xinluzao No.51 were selected and the sample-making methods were studied; then the refractive and absorption curves of samples were got and given a detailed discussion; finally, absorption index of transgenic and nontransgenic DNA was observed and discussed. The results showed there were small fluctuations in THz band, and refractive index of transgenic seeds was lower than nontransgenic ones and had obvious turning point at 1.4-2.0 THz region. There were significant peaks in 1.0-1.2 and 1.3-1.5 THz regions for the transgenic cotton seeds. Transgenic DNA had higher absorption index than nontransgenic DNA, and there were 3-4 peaks corresponding to the cotton seed samples in 1.0-1.6 THz region. These results showed cotton seeds samples can provide important bio-information in THz band, and this study provided a basis for developing potential THz-based gene detection technologies.

Near-Infrared Spectroscopy Coupled Chemometric Algorithms for Rapid Origin Identification and Lipid Content Detection of Pinus Koraiensis Seeds

Lipid content is an important indicator of the edible and breeding value of Pinus koraiensis seeds. Difference in origin will affect the lipid content of the inner kernel, and neither can be judged by appearance or morphology. Traditional chemical methods are small-scale, time-consuming, labor-intensive, costly, and laboratory-dependent. In this study, near-infrared (NIR) spectroscopy combined with chemometrics was used to identify the origin and lipid content of P. koraiensis seeds. Principal component analysis (PCA), wavelet transformation (WT), Monte Carlo (MC), and uninformative variable elimination (UVE) methods were used to process spectral data and the prediction models were established with partial least-squares (PLS). Models were evaluated by R2 for calibration and prediction sets, root mean standard error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP). Two dimensions of input data produced a faster and more accurate PLS model. The accuracy of the calibration and prediction sets was 98.75% and 97.50%, respectively. When the Donoho Thresholding wavelet filter 'bior4.4' was selected, the WT-MC-UVE-PLS regression model had the best predictions. The R2 for the calibration and prediction sets was 0.9485 and 0.9369, and the RMSECV and RMSEP were 0.0098 and 0.0390, respectively. NIR technology combined with chemometric algorithms can be used to characterize P. koraiensis seeds.

Rapid on-site detection of genetically modified soybean products by real-time loop-mediated isothermal amplification coupled with a designed portable amplifier

Herein, we developed a rapid method for detection of genetically modified soybean (GTS 40-3-2) products using loop-mediated isothermal amplification (LAMP). A crude 5-minute extraction method was established for DNA extraction from soybeans and soybean products. LAMP reaction for CaMV35S promoter was optimized and the fastest threshold time (Tt) was 14 min with 4 mM magnesium ions at 63 °C. A portable instrument was designed to perform real-time LAMP in the field. As little as 0.1% GM soybean, specifically, or 0.5% GM ingredients in Chinese traditional tofu could be detected in 30 min from sampling to results. We used this method to further assess other five soybean products to determine whether they contained transgenic ingredients and compared the results with those obtained using PCR, which suggested the proposed method was applicable for rapid detection of genetically modified soybean in food products.

Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics

Background: In recent years, genetically modified technology has developed rapidly, and the potential impact of genetically modified foods on human health and the ecological environment has received increasing attention. The currently used methods for testing genetically modified foods are cumbersome, time-consuming, and expensive. This paper proposed a more efficient and convenient detection method. Methods: Near-infrared diffuse reflectance spectroscopy (NIRDRS) combined with multivariate calibration methods, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), were used for identification of different rice varieties and transgenic (Bt63)/non-transgenic rice. Spectral pretreatment methods, including Norris–Williams smooth (NWS), standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay 1st derivative (SG 1st-Der), were used for spectral noise reduction and effective information enhancement. Accuracy was used to evaluate the qualitative discriminant models. Results: The results showed that the SG 1st-Der pretreatment method, combined with the SVM, provided the optimal model to distinguish different rice varieties. The accuracy of the optimal model was 98.33%. For the discrimination model of transgenic/non-transgenic rice, the SNV-SVM model, MSC-SVM model, and SG 1st-Der-PLS-DA model all achieved good analysis results with the accuracy of 100%. Conclusion: The results showed that portable NIR spectroscopy combined with chemometrics methods could be used to identify rice varieties and transgenic characteristics (Bt63) due to its fast, non-destructive, and accurate advantages.

Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis

There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS-DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.

Intravoxel Incoherent Motion Diffusion for Identification of Breast Malignant and Benign Tumors Using Chemometrics

The aim of the paper is to identify the breast malignant and benign lesions using the features of apparent diffusion coefficient (ADC), perfusion fraction f, pseudodiffusion coefficient D^⁎, and true diffusion coefficient D from intravoxel incoherent motion (IVIM). There are 69 malignant cases (including 9 early malignant cases) and 35 benign breast cases who underwent diffusion-weighted MRI at 3.0 T with 8 b-values (0~1000 s/mm²). ADC and IVIM parameters were determined in lesions. The early malignant cases are used as advanced malignant and benign tumors, respectively, so as to assess the effectiveness on the result. A predictive model was constructed using Support Vector Machine Binary Classification (SVMBC, also known Support Vector Machine Discriminant Analysis (SVMDA)) and Partial Least Squares Discriminant Analysis (PLSDA) and compared the difference between them both. The D value and ADC provide accurate identification of malignant lesions with b = 300, if early malignant tumor was considered as advanced malignant (cancer). The classification accuracy is 93.5% for cross-validation using SVMBC with ADC and tissue diffusivity only. The sensitivity and specificity are 100% and 87.0%, respectively, r²_cv = 0.8163, and root mean square error of cross-validation (RMSECV) is 0.043. ADC and IVIM provide quantitative measurement of tissue diffusivity for cellularity and are helpful with the method of SVMBC, getting comprehensive and complementary information for differentiation between benign and malignant breast lesions.

Discrimination of transgenic soybean seeds by terahertz spectroscopy

Discrimination of genetically modified organisms is increasingly demanded by legislation and consumers worldwide. The feasibility of a non-destructive discrimination of glyphosate-resistant and conventional soybean seeds and their hybrid descendants was examined by terahertz time-domain spectroscopy system combined with chemometrics. Principal component analysis (PCA), least squares-support vector machines (LS-SVM) and PCA-back propagation neural network (PCA-BPNN) models with the first and second derivative and standard normal variate (SNV) transformation pre-treatments were applied to classify soybean seeds based on genotype. Results demonstrated clear differences among glyphosate-resistant, hybrid descendants and conventional non-transformed soybean seeds could easily be visualized with an excellent classification (accuracy was 88.33% in validation set) using the LS-SVM and the spectra with SNV pre-treatment. The results indicated that THz spectroscopy techniques together with chemometrics would be a promising technique to distinguish transgenic soybean seeds from non-transformed seeds with high efficiency and without any major sample preparation.

Applications of near-infrared spectroscopy in food safety evaluation and control: a review of recent research advances

Food safety is a critical public concern, and has drawn great attention in society. Consequently, developments of rapid, robust, and accurate methods and techniques for food safety evaluation and control are required. As a nondestructive and convenient tool, near-infrared spectroscopy (NIRS) has been widely shown to be a promising technique for food safety inspection and control due to its huge advantages of speed, noninvasive measurement, ease of use, and minimal sample preparation requirement. This review presents the fundamentals of NIRS and focuses on recent advances in its applications, during the last 10 years of food safety control, in meat, fish and fishery products, edible oils, milk and dairy products, grains and grain products, fruits and vegetables, and others. Based upon these applications, it can be demonstrated that NIRS, combined with chemometric methods, is a powerful tool for food safety surveillance and for the elimination of the occurrence of food safety problems. Some disadvantages that need to be solved or investigated with regard to the further development of NIRS are also discussed.

Feasibility for non-destructive discrimination of natural and beryllium-diffused sapphires using Raman spectroscopy

Raman spectroscopy based non-destructive discrimination between natural and beryllium-diffused (Be-diffused) sapphires has been attempted. The initial examination of Raman image acquired on a sapphire revealed that microscopic structural and compositional heterogeneity was apparent in the sample, so acquisition of spectra able to represent a whole body of sapphire rather than a localized area was necessary for a reliable discrimination. For this purpose, a wide area illumination (WAI) scheme (illumination area: 28.3mm(2)) providing a large sampling volume was employed to collect representative Raman spectra of sapphires. Upon the diffusion of Be into a sapphire, the band shift originated from varied lattice structure by substitution of Be at cation sites was observed and utilized as a valuable spectral signature for the discrimination. In the domain of principal component (PC) scores, the groups of natural and Be-diffused sapphires were identifiable with minor overlapping and the cross-validated discrimination error was 7.3% when k-Nearest Neighbor (k-NN) was used as a classifier.

Discrimination of Transgenic Rice containing the Cry1Ab Protein using Terahertz Spectroscopy and Chemometrics

Spectroscopic techniques combined with chemometrics methods have proven to be effective tools for the discrimination of objects with similar properties. In this work, terahertz time-domain spectroscopy (THz-TDS) combined with discriminate analysis (DA) and principal component analysis (PCA) with derivative pretreatments was performed to differentiate transgenic rice (Hua Hui 1, containing the Cry1Ab protein) from its parent (Ming Hui 63). Both rice samples and the Cry1Ab protein were ground and pressed into pellets for terahertz (THz) measurements. The resulting time-domain spectra were transformed into frequency-domain spectra, and then, the transmittances of the rice and Cry1Ab protein were calculated. By applying the first derivative of the THz spectra in conjunction with the DA model, the discrimination of transgenic from non-transgenic rice was possible with accuracies up to 89.4% and 85.0% for the calibration set and validation set, respectively. The results indicated that THz spectroscopic techniques and chemometrics methods could be new feasible ways to differentiate transgenic rice.

Use of multispectral imaging in varietal identification of tomato

Multispectral imaging is an emerging non-destructive technology. In this work its potential for varietal discrimination and identification of tomato cultivars of Nepal was investigated. Two sample sets were used for the study, one with two parents and their crosses and other with eleven cultivars to study parents and offspring relationship and varietal identification respectively. Normalized canonical discriminant analysis (nCDA) and principal component analysis (PCA) were used to analyze and compare the results for parents and offspring study. Both the results showed clear discrimination of parents and offspring. nCDA was also used for pairwise discrimination of the eleven cultivars, which correctly discriminated upto 100% and only few pairs below 85%. Partial least square discriminant analysis (PLS-DA) was further used to classify all the cultivars. The model displayed an overall classification accuracy of 82%, which was further improved to 96% and 86% with stepwise PLS-DA models on high (seven) and poor (four) sensitivity cultivars, respectively. The stepwise PLS-DA models had satisfactory classification errors for cross-validation and prediction 7% and 7%, respectively. The results obtained provide an opportunity of using multispectral imaging technology as a primary tool in a scientific community for identification/discrimination of plant varieties in regard to genetic purity and plant variety protection/registration.

Rapid Discrimination of Apple Varieties via Near-Infrared Reflectance Spectroscopy and Fast Allied Fuzzy C-Means Clustering

Discrimination of apple varieties plays an important role in apple post-harvest commercial processing. A fast allied fuzzy c-means (FAFCM) clustering algorithm was proposed to classify the apple varieties using near-infrared reflectance (NIR) spectroscopy technology and orthogonal linear discriminant analysis (OLDA) which was used as feature extraction and dimensionality reduction method. Our classification method: the high-dimensional NIR data were reduced to three-dimensional data by OLDA at first, and the FAFCM clustering algorithm was implemented to classify the reduced data. Furthermore, the principal component analysis (PCA) and linear discriminant analysis (LDA) combined with k-nearest neighbor classifier (KNNC), fuzzy c-means (FCM) clustering and unsupervised possibilistic clustering algorithm (UPCA), formed the other four classification methods to classify apple samples in comparison with our proposed method. The experimental results showed that FAFCM achieved the best performance of classification.

The detection of T-Nos, a genetic element present in GMOs, by cross-priming isothermal amplification with real-time fluorescence

An isothermal cross-priming amplification (CPA) assay for Agrobacterium tumefaciens nopaline synthase terminator (T-Nos) was established and investigated in this work. A set of six specific primers, recognizing eight distinct regions on the T-Nos sequence, was designed. The CPA assay was performed at a constant temperature, 63 °C, and detected by real-time fluorescence. The results indicated that real-time fluorescent CPA had high specificity, and the limit of detection was 1.06 × 10(3) copies of rice genomic DNA, which could be detected in 40 min. Comparison of real-time fluorescent CPA and conventional polymerase chain reaction (PCR) was also performed. Results revealed that real-time fluorescent CPA had a comparable sensitivity to conventional real-time PCR and had taken a shorter time. In addition, different contents of genetically modified (GM)-contaminated rice seed powder samples were detected for practical application. The result showed real-time fluorescent CPA could detect 0.5 % GM-contaminated samples at least, and the whole reaction could be finished in 35 min. Real-time fluorescent CPA is sensitive enough to monitor labeling systems and provides an attractive method for the detection of GMO.

New discrimination method combining hit quality index based spectral matching and voting

A new discrimination method, called hit quality index (HQI)-voting, that uses the HQI for discriminant analysis has been developed. HQI indicates the degree of spectral matching between two spectra as known. In this method, a library sample yielding the highest HQI value for an unknown sample was initially searched and a group containing this sample was chosen as the group for the unknown sample. When overall spectral features of two groups are quite close to each other, many library samples with similar HQI values could be available for an unknown sample. In this situation, the simultaneous consideration of multiple votes (several library samples with close HQI values) for final decision would be more robust. In order to evaluate the discrimination performance of HQI-voting, three different near-infrared (NIR) spectroscopic datasets composed of two sample groups were used: (1) domestic and imported sesame samples, (2) domestic and imported Angelica gigas samples, and (3) diesel and light gas oil (LGO) samples. For the purpose of comparison, principal component analysis-linear discriminant analysis (PCA-LDA), partial least squares-discriminant analysis (PLS-DA) as well as k-nearest neighbor (k-NN) were also performed using the same datasets and the resulting accuracies were compared. The discrimination performances improved with the use of HQI-voting in comparison with those resulted from PCA-LDA and PLS-DA. The overall results support that HQI-voting is a comparable discrimination method to that of existing factor-based multivariate methods.

Direct detection of phoxim in water by two-dimensional correlation near-infrared spectroscopy combined with partial least squares discriminant analysis

This paper has established a simple method to detect directly phoxim in water. In the light of two-dimensional correlation analysis, the band of wavenumber for near-infrared spectroscopy of the model is between 5364.8 and 7552.9 cm(-1), the rate of accuracy for partial least squares discriminant analysis to calibration set (n=149) is 100%, prediction set (n=75) is 93.3% and the overall rate of accuracy for all the samples is 97.8% under the limit of detection 1 μg ml(-1) owing to the spectra preprocessing by standard normal variate transformation and multiplicative scatter correction. It is made clear that this method (two-dimensional correlation analysis combined with partial least squares discriminant analysis) is effective to detect directly phoxim in water.

Determination of amino acids in Chinese rice wine by fourier transform near-infrared spectroscopy

Chinese rice wine is abundant in amino acids. The possibility of quantitative detection of 16 free amino acids (aspartic acid, threonine, serine, glutamic acid, proline, glycine, alanine, valine, methionine, isoleucine, leucine, tyrosine, phenylalanine, lysine, histidine, and arginine) in Chinese rice wine by Fourier transform near-infrared (NIR) spectroscopy was investigated for the first time in this study. A total of 98 samples from vintage 2007 rice wines with different aging times were analyzed by NIR spectroscopy in transmission mode. Calibration models were developed using partial least-squares regression (PLSR) with high-performance liquid chromatography (HPLC) by postcolumn derivatization and diode array detection as a reference method. To validate the calibration models, full cross (leave-one-out) validation was employed. The results showed that the calibration statistics were good (rcal>0.94) for all amino acids except proline, histidine, and arginine. The correlation coefficient in cross validation (rcv) was >0.81 for 12 amino acids. The residual predictive deviation (RPD) value obtained was >1.5 in all amino acids except proline and arginine, and it was >2.0 in 6 amino acids. The results obtained in this study indicated that NIR spectroscopy could be used as an easy, rapid, and novel tool to quantitatively predict free amino acids in Chinese rice wine without sophisticated methods.