Prediction of the geographical origin of butters by partial least square discriminant analysis (PLS-DA) applied to infrared spectroscopy (FTIR) data

Geographical authentication of saffron by chemometrics applied to the ion mobility spectrometry data

There is a lack of a reliable tool for quickly determining the geographical origins of saffron (SFR). Ion mobility spectrometry (IMS) has emerged as a promising method for rapid authentication. In this study, 232 Iranian SFR samples harvested in five distinct areas (Khorasan, Azerbaijan, Golestan, Fars, and Isfahan) were analyzed by IMS coupled with chemometric methods. The principal component analysis (PCA) was applied for analyzing the collected IMS data, utilizing three principle components (PCs) that accounted for 81 % of the explained variance. Moreover, the partial least squares-discriminant analysis (PLS-DA) demonstrated the average sensitivity and specificity rates, of 72.3 % to 92.5 % for the exernal test set and 75.5 % to 94.3 % for training set. The accuracy values were ≥ 85.0 % for the prediction set for all classes of samples. The results of this study revealed a successful application of IMS and chemometric methods for rapid geographical authentication of saffron samples in Iran.

Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis

BACKGROUND

Enzyme immobilization is a beneficial component involved in biocatalytic strategies. Understanding and evaluating the enzyme immobilization system plays an important role in the successful development and implementation of the biocatalysis route. Ensuring the implementation of a successful enzyme immobilization process is vital for realizing a highly functioning and well suited biocatalytic process within pharmaceutical development.

AIM

To develop a method which can accurately and objectively identify and classify differences within enzyme immobilization systems, sample preparation methods, and data collection parameters.

METHODS

Raman hyperspectral imaging was used to obtain a total of eight spectral data sets from enzyme immobilization samples. Partial least squares discriminant analysis (PLS-DA) was used to classify and identify the samples based on their differences.

RESULTS

Several two-class, four-class, and eight-class PLS-DA models were built to classify the different sample data sets. All models reached between 92-100% accuracy after cross-validation and external validation, illustrating great success of the models for identifying differences between the samples.

CONCLUSION

Raman hyperspectral imaging with machine learning can be used to investigate, interpret, and classify different data collection parameters, sample preparation methods, and enzyme immobilization supports, providing crucial insight into enzyme immobilization process development.

A review of conventional and rapid analytical techniques coupled with multivariate analysis for origin traceability of soybean

Soybean has developed a reputation as a superfood due to its nutrient profile, health benefits, and versatility. Since 1960, its demand has increased dramatically, going from a mere 17 MMT to almost 358 MMT in the production year 2021/22. These extremely high production rates have led to lower-than-expected product quality, adulteration, illegal trade, deforestation, and other concerns. This necessitates the development of an effective technology to confirm soybean's provenance. This is the first review that investigates current analytical techniques coupled with multivariate analysis for origin traceability of soybeans. The fundamentals of several analytical techniques are presented, assessed, compared, and discussed in terms of their operating specifics, advantages, and shortcomings. Additionally, significance of multivariate analysis in analyzing complex data has also been discussed.

Geographical Discrimination of Ground Amazon Cocoa by Near-Infrared Spectroscopy: Influence of Sample Preparation

This work presents the application of the NIR technique associated with exploratory analysis of spectral data by main principal components for the discrimination of Amazon cocoa ground seeds. Cocoa samples from different geographic regions of the state of Pará, Brazil (Medicilândia, Tucumã, and Tomé-Açu), were evaluated. The samples collected from each region were divided into four groups distinguished by the treatment applied to the samples, which were fermented (1-with fat and 2-fat-free) and unfermented (3-with moisture and 4-dried). Each set of samples was analyzed separately to identify the influence of moisture, fermentation, and fat on the geographical differentiation of the three regions. From the results obtained, it can be observed that it was not possible to differentiate the samples of seeds not fermented by geographic origin. However, fermentation was crucial for efficient discrimination, providing more defined clusters for each geographic region. The presence of fat in the seeds was a determinant to obtain the best model of geographic discrimination.

Simultaneous detection for adulterations of maltodextrin, sodium carbonate, and whey in raw milk using Raman spectroscopy and chemometrics

To achieve rapid on-site identification of raw milk adulteration and simultaneously quantify the levels of various adulterants, we combined Raman spectroscopy with chemometrics to detect 3 of the most common adulterants. Raw milk was artificially adulterated with maltodextrin (0.5-15.0%; wt/wt), sodium carbonate (10-100 mg/kg), or whey (1.0-20.0%; wt/wt). Partial least square discriminant analysis (PLS-DA) classification and a partial least square (PLS) regression model were established using Raman spectra of 144 samples, among which 108 samples were used for training and 36 were used for validation. A model with excellent performance was obtained by spectral preprocessing with first derivative, and variable selection optimization with variable importance in the projection. The classification accuracy of the PLS-DA model was 95.83% for maltodextrin, 100% for sodium carbonate, 95.84% for whey, and 92.25% for pure raw milk. The PLS model had a detection limit of 1.46% for maltodextrin, 4.38 mg/kg for sodium carbonate, and 2.64% for whey. These results suggested that Raman spectroscopy combined with PLS-DA and PLS model can rapidly and efficiently detect adulterants of maltodextrin, sodium carbonate, and whey in raw milk.

The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750–400, 1,450–1,000, and 1,150–1,000 cm^–1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750–400-cm^–1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150–1,000-cm^–1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.

Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy

Although Hom Mali rice is considered the highest quality rice in Thailand, it is susceptible to adulteration and substitution. There is a need for rapid, low-cost and efficient analytical techniques for monitoring the authenticity and geographical origin of Thai Hom Mali rice. In this study, two infrared spectroscopy techniques, Fourier-transform infrared spectroscopy with attenuated total reflection (FTIR-ATR) and near-infrared (NIR) spectroscopy, were applied and compared for the differentiation of Thai Hom Mali rice from two geographical regions over two production years. The Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model, built using spectral data from the benchtop FTIR-ATR, achieved 96.97% and 100% correct classification of the test dataset for each of the production years, respectively. The OPLS-DA model, built using spectral data from the portable handheld NIR, achieved 84.85% and 86.96% correct classification of the test dataset for each of the production years, respectively. Direct NIR analysis of the polished rice grains (i.e., no sample preparation) was determined as reliable for analysis of ground rice samples. FTIR-ATR and NIR spectroscopic analysis both have significant potential as screening tools for the rapid detection of fraud issues related to the geographical origin of Thai Hom Mali rice.

Effects of Urbanization on Ecosystem Services in the Shandong Peninsula Urban Agglomeration, in China: The Case of Weifang City

Ecosystem services are the material basis of economic and social development, and play essential roles in the sustainable development of ecosystems. Urbanization can remarkably alter the provision of ecosystem services. Most studies in this area have focused on densely populated metropolises with poor ecological environments, while comparatively few studies have focused on cities with low ecological pressures. Therefore, to avoid continuing to engage in the repetitive pattern of destroying first and rehabilitating later, quantitative analyses of urbanization and ecosystem services should be carried out in representative cities. In this study, based on partial least squares-discriminant analysis, kernel density estimation, and correlation analysis, we quantitatively evaluated the impact of urbanization on ecosystem services in Weifang city. The Data Center for Resources and Environmental Sciences at the Chinese Academy of Sciences and the Institute of Geographic Sciences and Natural Resources Research provided remote sensing data on land use, the gross domestic production (GDP), population data, and ecosystem services. The results were as follows: (1) The variation in population, GDP, and built-up areas consistently increased throughout the study period, whereas the ecosystem service values (ESVs) decreased; (2) food production, raw material production, nutrient cycle maintenance, and soil conservation were decisive ecosystem services that led to vast reductions in ESVs during the process of urbanization; and (3) the negative correlation coefficient between built-up areas and ecosystem services was greater than that between the population or GDP and ecosystem services, which indicated that the impacts of population and economic urbanization on ecosystem services lagged behind the impact of land urbanization. This study provides references for fully recognizing the ecological effects of urbanization, and make suggestions regarding the application of ecosystem services in sustainable development.

Authentication of carioca common bean cultivars (Phaseolus vulgaris L.) using digital image processing and chemometric tools

Bean authentication can result in higher quality products for commerce. Partial least squares discriminant analysis (PLS-DA) was applied to digital images in order to develop a methodology that allows the non-destructive discrimination of three Phaseolus vulgaris L. cultivars (Agro ANfc9, IPR-Andorinha, and IPR-Sabiá) having different technological characteristics. Principal component analysis resulted in a separation of these cultivars, but with a certain amount of overlap. Supervised analysis showed that three PLS1-DA models, each for two cultivars, was moderately better than the simultaneous treatment of all three cultivars (PLS2-DA). Permutation test evaluated statistical significance of PLS-DA models. The classification models were more accurate for Agro ANfc9 and IPR-Sabiá cultivars than for IPR-Andorinha. The Agro ANfc9-IPR-Sabiá model correctly classified 100% of the two bean classes in both training and test sets. This analytical strategy is fast, inexpensive, environmentally friendly, and can be applied for bean quality control helping cultivar authenticity for commerce.

Application of Mid-Infrared Portable Spectrometer for the Rapid Determination of Trans-Fatty Acid Content in Lipid Extracts of Snack and Bakery Products

In 2015, the US Food and Drug Administration passed a ban on the "generally recognized as safe" status of partially hydrogenated oils (PHOs), and in June 2018, PHOs were prohibited from being used. Our objective was to develop a predictive model to quantify trans-fat concentrations in bakery and snacks products using a portable mid-infrared (MIR) spectrometer. The approach was tested using 24 calibration standards (consisting of trielaidin in triolein and tripalmitin) and 87 bakery and snack products ranging from ND to 65% trans-fat. The fat was extracted by grinding products into powders and extracting the fat using petroleum ether. Gas Chromatography (AOCS Cd 14c-94) was used to determine the fatty acid profile and trans-fat content. Spectra were acquired by directly placing the fat (200 μL) onto the heated (65 ± 1°C) 5-reflection ZnSe crystal of a portable MIR spectrometer. Partial least squares regression (PLSR) models were developed using the calibration standards and extracted fats spectra targeting the signal of the C-H out-of-plane deformation band at 966 cm-1. Best model performances were obtained using the spectra of the extracted fat from bakery and snack products with the standard error of prediction of 0.5 g of trans-fats per 100 g of fat. We found that 25% of products labeled as zero trans-fat/serving did not comply with the maximum tolerance levels based on GC-FAME analysis. Portable FTIR devices operating in attenuated total reflection (ATR) mode can provide the food industry and government food inspectors with rapid, accurate, and high throughput measurements for routine screening to facilitate regulatory compliance.

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70-100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.

Comprehensive Review on Application of FTIR Spectroscopy Coupled with Chemometrics for Authentication Analysis of Fats and Oils in the Food Products

Currently, the authentication analysis of edible fats and oils is an emerging issue not only by producers but also by food industries, regulators, and consumers. The adulteration of high quality and expensive edible fats and oils as well as food products containing fats and oils with lower ones are typically motivated by economic reasons. Some analytical methods have been used for authentication analysis of food products, but some of them are complex in sampling preparation and involving sophisticated instruments. Therefore, simple and reliable methods are proposed and developed for these authentication purposes. This review highlighted the comprehensive reports on the application of infrared spectroscopy combined with chemometrics for authentication of fats and oils. New findings of this review included (1) FTIR spectroscopy combined with chemometrics, which has been used to authenticate fats and oils; (2) due to as fingerprint analytical tools, FTIR spectra have emerged as the most reported analytical techniques applied for authentication analysis of fats and oils; (3) the use of chemometrics as analytical data treatment is a must to extract the information from FTIR spectra to be understandable data. Next, the combination of FTIR spectroscopy with chemometrics must be proposed, developed, and standardized for authentication and assuring the quality of fats and oils.

Determination of milk fat authenticity in ultra-filtered white cheese by using Raman spectroscopy with multivariate data analysis

Cheese is one of the most widely consumed food products in the world. However, the increasing demand for nutritionally enhanced or functional products by the cheese industry has created new approaches that partially or fully replace milk fat. With this, new methods of adulteration have also been noted, potentially leading to these fully/partially-replaced products being offered as cheese. In this study, Raman spectroscopy was used to determine origins of fats in margarine, corn, and palm oils present in white and ultra-filtered cheese samples. Raman spectra were evaluated with partial least square-discriminant (PLS-DA) and PLS to identify fat/oil origins and adulteration ratios. The coefficients of determination and limits of detection for margarine, and corn and palm oil adulteration were found to be 0.990, 0.993, 0.991 and 3.38%, 3.36% and 3.59%, respectively.

Reliable Discrimination of Green Coffee Beans Species: A Comparison of UV-Vis-Based Determination of Caffeine and Chlorogenic Acid with Non-Targeted Near-Infrared Spectroscopy

Species adulteration is a common problem in the coffee trade. Several attempts have been made to differentiate among species. However, finding an applicable methodology that would consider the various aspects of adulteration remains a challenge. This study investigated an ultraviolet–visible (UV-Vis) spectroscopy-based determination of caffeine and chlorogenic acid contents, as well as the applicability of non-targeted near-infrared (NIR) spectroscopy, to discriminate between green coffee beans of the Coffea arabica (Arabica) and Coffea canephora (Robusta) species from Java Island, Indonesia. The discrimination was conducted by measuring the caffeine and chlorogenic acid content in the beans using UV-Vis spectroscopy. The data related to both compounds was processed using linear discriminant analysis (LDA). Information about the diffuse reflectance (log 1/R) spectra of intact beans was determined by NIR spectroscopy and analyzed using multivariate analysis. UV-Vis spectroscopy attained an accuracy of 97% in comparison to NIR spectroscopy’s accuracy by selected wavelengths of LDA (95%). The study suggests that both methods are applicable to discriminate reliably among species.

FTIR-ATR Spectroscopy Combined with Multivariate Regression Modeling as a Preliminary Approach for Carotenoids Determination in Cucurbita spp

Quantitative analysis of carotenoids has been extensively reported using UV-Vis spectrophotometry and chromatography, instrumental techniques that require complex extraction protocols with organic solvents. Fourier transform infrared spectroscopy (FTIR) is a potential alternative for simplifying the analysis of food constituents. In this work, the application of FTIR with attenuated total reflectance (ATR) was evaluated for the determination of total carotenoid content (TCC) in Cucurbita spp. samples. Sixty-three samples, belonging to different cultivars of butternut squash (C. moschata) and pumpkin (C. maxima), were selected and analyzed with FTIR- ATR (attenuated total reflectance). Three different preparation protocols for samples were followed: homogenization (A), freeze-drying (B), and solvent extraction (C). The recorded spectra were used to develop regression models by Partial Least Squares (PLS), using data from TCC, determined by UV-Vis spectrophotometry. The PLS regression model obtained with the FTIR data from the freeze-dried samples, using the spectral range 920–3000 cm−1, had the best figures of merit (R2CAL of 0.95, R2PRED of 0.93 and RPD of 3.78), being reliable for future application in agriculture. This approach for carotenoid determination in pumpkin and squash avoids the use of organic solvents. Moreover, these results are a rationale for further exploring this technique for the assessment of specific carotenoids in food matrices.

Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics

The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near-infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA-SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades.

Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy

Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laser-induced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues. Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily applied for rice certification.

A review of Fourier Transform Infrared (FTIR) spectroscopy used in food adulteration and authenticity investigations

The increasing demand for food and the globalisation of the supply chain have resulted in a rise in food fraud, and recent high profile cases, such as the Chinese milk scandal in 2008 and the EU horsemeat scandal in 2013 have emphasised the vulnerability of the food supply system to adulteration and authenticity frauds. Fourier Transform Infrared (FTIR) spectroscopy is routinely used in cases of suspected food fraud as it offers a rapid, easy and reliable detection method for these investigations. In this review, we first present a brief summary of the concepts of food adulteration and authenticity as well as a discussion of the current legislation regarding these crimes. Thereafter, we give an extensive overview of FTIR as an analytical technique and the different foods where FTIR analysis has been employed for food fraud investigations as well as the subsequent multivariate data analyses that have been applied successfully to investigate the case of adulteration or authenticity. Finally, we give a critical discussion of the applications and limitations of FTIR, either as a standalone technique or incorporated in a test battery, in the fight against food fraud.

Evaluation of human breastmilk adulteration by combining Fourier transform infrared spectroscopy and partial least square modeling

A two-step chemometric procedure was developed on the attenuated total reflection-Fourier transform infrared data of human breastmilk to detect adulteration by water or cow milk. The samples, collected from a Milk Bank, were analyzed before and after adulteration with whole, skimmed, semi-skimmed cow milk and water. A preliminary clustering via principal component analysis distinguished three classes: pure milk, milk adulterated with water, and milk adulterated with cow milk. A first partial least square-discriminant analysis (PLS-DA) classification model was built and then applied on new samples to identify the specific adulterants. The external validation on this model reached 100% of the correct identification of pure milk and 90% of the type of adulterants. In the following step, four PLS calibration models were built to quantify the amount of the adulterant detected in the classification analysis. The prediction performance of these models on new samples showed satisfactory parameters with root mean square error of prediction and percentage relative error lower than 1.38% and 3.31%, respectively.

The characterisation of Mozzarella cheese microstructure using high resolution synchrotron transmission and ATR-FTIR microspectroscopy

Synchrotron Fourier transform infrared (S-FTIR) microspectroscopy allows the label-free examination of material microstructure but has not been widely applied to dairy products. Here, S-FTIR microspectroscopy was applied to observe the microstructure of Mozzarella cheese and assess the protein and lipid distribution within individual cheese blocks. High lipid and high protein areas were identified in transmission and attenuated total reflectance (ATR) analysis modes and the secondary structures of cheese proteins determined. Hierarchical cluster analysis and principal component analysis identified variation in random coil, water content, lipid carbonyl and methylene stretching across the sampled area. Similar spectral features were obtained in both analysis modes; spatial resolution was higher with ATR and small differences were noted, potentially as a result of differences in sample preparation. S-FTIR is a useful microscopy tool that can detect structural alterations that may affect product properties and may assist reverse engineering of a range of dairy products.

Spatiotemporal Variation Characteristics of Ecosystem Service Losses in the Agro-Pastoral Ecotone of Northern China

Being subject to climate change and human intervention, the land-use pattern in the agro-pastoral ecotone of Northern China has undergone complex changes over the past few decades, which may jeopardize the provision of ecosystem services. Thus, for sustainable land management, ecosystem services should be evaluated and monitored. In this study, based on Landsat TM/ETM data, we quantitatively evaluated the losses of ecosystem service values (ESV) in three sections of the agro-pastoral ecotone from 1980-2015. The results were as follows: (1) the main characteristic of the land conversions was that a large area of grassland was converted into cultivated land in the agro-pastoral ecotone; (2) on the spatial scale, the ESV losses of the agro-pastoral ecotone can be called an "inclined surface" in the direction of the northeast to southwest, and the northeastern section of the agro-pastoral ecotone lost more ESV than the middle and northwest sections (p < 0.05), on the temporal scale, the order of losses was 1990-2000 > 1980-1990 > 2000-2015; (3) the agro-pastoral ecotone lost more ESV, which was mainly due to four kinds of land conversion, which were grassland that was transformed into cultivated land, grassland transformed into unused land, grassland transformed into built-up areas, and cultivated land transformed into built-up areas; (4) although these land conversions were curbed after the implementation of protection policies at the end of the 1990s, due to reduced precipitation and increasing temperatures, the agro-pastoral ecotone will face a more severe situation in the future; and, (5) during the period of 1990-2015, the overall dynamic processes of increasing population gradually expanded to the sparsely populated pastoral area. Therefore, we believe that human interventions are the main cause of ecological deterioration in the agro-pastoral ecotone. This study provides references for fully understanding the regional differences in the ecological and environmental effects of land use change and it helps to objectively evaluate ecological civilization construction in the agro-pastoral ecotone of Northern China.

Identification of cow, buffalo, goat and ewe milk species in fermented dairy products using synchronous fluorescence spectroscopy

In the dairy industry, substitution of high priced milk species with low priced ones is a common practice, and determination of milk species is critical. In this study, synchronous fluorescence spectroscopy (SFS) method was developed for identification of milk species in fermented dairy products (yoghurt and cheese). Three partial least square-discriminant analysis models were developed in order to identify pure-mixed samples, milk species and binary mixture type, and partial least square (PLS) model was utilized to quantify the mixing ratio in binary mixtures. PLS models used for yoghurt and cheese samples showed that detection limits of adulteration were below 3.3%. Apart from the buffalo-cow yoghurt and goat-cow cheese, precision of the measurements was found to be below 6.2. It can be said that SFS technique is applicable on yoghurt and cheese samples as it's a less destructive and a less costly method compared to DNA and protein based conventional methods.

Simultaneous Determination of Several Fiber Contents in Blended Fabrics by Near-Infrared Spectroscopy and Multivariate Calibration

The qualitative and quantitative determination of the components of textile fibers takes an important position in quality control. A fast and nondestructive method of simultaneously analyzing four fiber components in blended fabrics was studied by near-infrared (NIR) spectroscopy combined with multivariate calibration. Two sample sets including 39 and 25 samples were designed by simplex mixture lattice design methods and used for experiment. Four components include wool, polyester, polyacrylonitrile, and nylon and their mixture is one of the most popular formulas of textiles. Uninformative variable elimination-partial least squares (UVEPLS) and the full-spectrum partial least squares (PLS) were used as the tool. On the test set, the mean standard error of prediction (SEP) and the mean ratio of the standard deviation of the response variable and SEP (RPD) of the full-spectrum PLS model and UVEPLS model were 0.38, 0.32 and 7.6, 8.3, respectively. This result reveals that the UVEPLS can construct local models with acceptable and better performance than the full-spectrum PLS. It indicates that this method is valuable for nondestructive analysis in the field of wool content detection since it can avoid time-consuming, costly, and laborious wet chemical analysis.

Targeted and untargeted LC-MS polyphenolic profiling and chemometric analysis of propolis from different regions of Croatia

Propolis is a complex biological matrix consisting mostly of plant resins and waxes, and in a small proportion of the herbal secondary metabolites, phenols. Phenols are components that are responsible for biological activities of propolis, however, their qualitative and quantitative composition is strongly influenced by climate and vegetation. Although studies on profiling of propolis samples from different countries have been carried out for some time propolis from Croatia is still not characterized till now. Targeted liquid chromatography coupled to triple quadrupole (LC-QQQ), untargeted liquid chromatography coupled to quadrupole time-of-flight (LC-QTOF) and direct injection QTOF methods were developed and 56 propolis samples from different geographical regions of Croatia were analyzed. Results revealed that there is not only one expected type of propolis in the territory of Croatia; i.e. beside expected European "poplar" propolis another type can be distinguished. Principal component analysis (PCA) and Partial least squares Discriminant Analysis (PLS-DA) indicated that the phenolic content of propolis samples significantly changes under the influence of the Mediterranean, so the "European" propolis type mixes with the Mediterranean type on the Croatian coast, especially on the islands. For fast screening of propolis type, direct injection QTOF analysis demonstrated to be fast and reliable method, but for unambiguous identification of phenolic compounds, chromatographic separation is indispensable. This paper presents the findings from the first research on phenolic profiling of propolis from Croatia.

Quantitative determination of wool in textile by near-infrared spectroscopy and multivariate models

The wool content in textiles is a key quality index and the corresponding quantitative analysis takes an important position due to common adulterations in both raw and finished textiles. Conventional methods are maybe complicated, destructive, time-consuming, environment-unfriendly. Developing a quick, easy-to-use and green alternative method is interesting. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and several partial least squares (PLS)-based algorithms and elastic component regression (ECR) algorithms for measuring wool content in textile. A total of 108 cloth samples with wool content ranging from 0% to 100% (w/w) were collected and all the compositions are really existent in the market. The dataset was divided equally into the training and test sets for developing and validating calibration models. When using local PLS, the original spectrum axis was split into 20 sub-intervals. No obvious difference of performance can be seen for the local PLS models. The ECR model is comparable or superior to the other models due its flexibility, i.e., being transition state from PCR to PLS. It seems that ECR combined with NIR technique may be a potential method for determining wool content in textile products. In addition, it might have regulatory advantages to avoid time-consuming and environmental-unfriendly chemical analysis.

Overoptimism in cross-validation when using partial least squares-discriminant analysis for omics data: a systematic study

Advances in analytical instrumentation have provided the possibility of examining thousands of genes, peptides, or metabolites in parallel. However, the cost and time-consuming data acquisition process causes a generalized lack of samples. From a data analysis perspective, omics data are characterized by high dimensionality and small sample counts. In many scenarios, the analytical aim is to differentiate between two different conditions or classes combining an analytical method plus a tailored qualitative predictive model using available examples collected in a dataset. For this purpose, partial least squares-discriminant analysis (PLS-DA) is frequently employed in omics research. Recently, there has been growing concern about the uncritical use of this method, since it is prone to overfitting and may aggravate problems of false discoveries. In many applications involving a small number of subjects or samples, predictive model performance estimation is only based on cross-validation (CV) results with a strong preference for reporting results using leave one out (LOO). The combination of PLS-DA for high dimensionality data and small sample conditions, together with a weak validation methodology is a recipe for unreliable estimations of model performance. In this work, we present a systematic study about the impact of the dataset size, the dimensionality, and the CV technique used on PLS-DA overoptimism when performance estimation is done in cross-validation. Firstly, by using synthetic data generated from a same probability distribution and with assigned random binary labels, we have obtained a dataset where the true classification rate (CR) is 50%. As expected, our results confirm that internal validation provides overoptimistic estimations of the classification accuracy (i.e., overfitting). We have characterized the CR estimator in terms of bias and variance depending on the internal CV technique used and sample to dimensionality ratio. In small sample conditions, due to the large bias and variance of the estimator, the occurrence of extremely good CRs is common. We have found that overfitting peaks when the sample size in the training subset approaches the feature vector dimensionality minus one. In these conditions, the models are neither under- or overdetermined with a unique solution. This effect is particularly intense for LOO and peaks higher in small sample conditions. Overoptimism is decreased beyond this point where the abundance of noisy produces a regularization effect leading to less complex models. In terms of overfitting, our study ranks CV methods as follows: Bootstrap produces the most accurate estimator of the CR, followed by bootstrapped Latin partitions, random subsampling, K-Fold, and finally, the very popular LOO provides the worst results. Simulation results are further confirmed in real datasets from mass spectrometry and microarrays.

Study on the Characteristic Spectral Properties for Species Identification of Animal-Derived Feedstuff Using Fourier Transform Infrared Spectroscopy

The objective of the present study was to explore the effective spectral bands related to lipid characteristics in spectra of raw animal-derived feedstuff and figure out which marked spectral regions (single or combined) contributed more to species discrimination. A total of 82 meat and bone meals, including porcine, poultry, bovine, ovine, and fish, were studied. Raw materials, extracted lipid, and defatted samples were simultaneously analyzed and calculated using Fourier transform infrared (FT-IR) spectroscopy in combination with chemometric methods. Taking the spectra of lipid as references, five marked spectral regions considered the main lipid characteristic regions were found in the raw animal-derived feedstuff spectra. In the study, single and combined marked spectral bands were investigated and proved to have better performance than the whole spectra of raw terrestrial animal-derived feedstuff and fishmeal. For the discrimination of five animal species, the regions of 1800-1650 cm^-1, 1500-1330 cm^-1, 1260-1060 cm^-1, and 790-640 cm^-1 presented better results; for the classification of three categories, the regions of 3100-2800 cm^-1, 1800-1650 cm^-1, and 1500-1330 cm^-1 showed the best results.

Modern analytical methods for the detection of food fraud and adulteration by food category

This review provides current information on the analytical methods used to identify food adulteration in the six most adulterated food categories: animal origin and seafood, oils and fats, beverages, spices and sweet foods (e.g. honey), grain-based food, and others (organic food and dietary supplements). The analytical techniques (both conventional and emerging) used to identify adulteration in these six food categories involve sensory, physicochemical, DNA-based, chromatographic and spectroscopic methods, and have been combined with chemometrics, making these techniques more convenient and effective for the analysis of a broad variety of food products. Despite recent advances, the need remains for suitably sensitive and widely applicable methodologies that encompass all the various aspects of food adulteration. © 2017 Society of Chemical Industry.

Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM)

In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms.

Data fusion of near-infrared and mid-infrared spectra for identification of rhubarb

Rhubarb has different medicinal efficacy to official rhubarb and may affect the clinical medication safety. In order to guarantee the quality of rhubarb, we established a method to distinguish unofficial rhubarbs. 52 official and unofficial rhubarb samples were analyzed using near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy for classification. The feature vectors, which were selected by wavelet compression (WC) and interval partial least squares (iPLS) from NIR, MIR spectra, were fused together for identifying rhubarb samples. Partial least squares-discriminant analysis (PLS-DA), soft independent modeling of class analogies (SIMCA), support vector machine (SVM) and artificial neural network (ANN) were compared for classifying rhubarb. The use of data fusion strategies improved the classification model and allowed correct classification of all the samples.