Optimization of discriminant partial least squares regression models for the detection of animal by-product meals in compound feedingstuffs by near-infrared spectroscopy

Non-destructive Near Infrared Spectroscopy for the labelling of frozen Iberian pork loins

Iberian pigs fed on acorns and pasture were slaughtered from January until March of 2018 and 2019. The meat from those Iberian pigs is a seasonal food that only can be found fresh, at the marketplace, during a limit period of the year. Selling frozen-thawed meat is a legal practice, but consumers must be informed about it on the product label. However, to declare as fresh meat, meat previously frozen, is one of the most frequent meat frauds. The present study compares the performance of two rather different Near Infrared Spectroscopy instruments, based on Fourier Transform and Linear Variable Filter technologies, for the in-situ detection of fresh and frozen-thawed acorns-fed Iberian pig loins using Partial Least Discriminant Analysis (PLS-DA). The performance of the models developed for both instruments offered a very high discriminant ability. Furthermore, the models showed consistent results and interpretation when were evaluated with several scalars and graphical methods.

Rapid, simultaneous, and in situ authentication and quality assessment of intact bell peppers using near-infrared spectroscopy technology

BACKGROUND

The ability of near-infrared (NIR) spectroscopy to authenticate individual bell peppers as a function of the growing system (outdoor or greenhouse) was tested using partial least squares discriminant analysis. Bell peppers grown outdoors (130 samples) or in a greenhouse (264 samples) during the 2015 and 2016 seasons were selected for this purpose and analysed using a portable, handheld, microelectromechanical system (MEMS) instrument MicroPhazir (spectral range 1600-2400 nm), working in reflectance. Subsequently, the potential of NIR spectroscopy as a non-destructive sensor for in situ quality (dry matter and soluble solid content) measurements, was investigated.

RESULTS

The models correctly classified 89.73% and 88.00% of the samples by growing system, when trained with unbalanced and balanced sets respectively, mainly due to the differences in physical-chemical attributes between bell peppers cultivated in the two growing systems. Separate classification models for bell peppers grouped by ripeness (judged by the colour), allowed the classification of 88.28-91.37% of the samples correctly. The standard error of cross-validation values for the quantitative models were 0.66% fresh weight and 0.75 °Brix for dry matter and soluble solid content, respectively.

CONCLUSIONS

The results showed that NIR spectroscopy can be used successfully for predicting the growing systems used in bell pepper production, which is of particular value to guarantee the authentication of outdoor-grown peppers. Additionally, the results showed that NIR spectroscopy can be used simultaneously as a rapid preliminary screening technique to measure quality. © 2018 Society of Chemical Industry.

Pre-harvest screening on-vine of spinach quality and safety using NIRS technology

The study sought to perform a non-destructive and in-situ quality evaluation of spinach plants using near infrared (NIR) spectroscopy in order to establish its suitability for different uses once harvested. Modified partial least square (MPLS) regression models using NIR spectra of intact spinach leaves were developed for nitrate, ascorbic acid and soluble solid contents. The residual predictive deviation (RPD) values were 1.29, 1.21 and 2.54 for nitrate, ascorbic acid and soluble solid contents, respectively. Later, this predictive capacity increased for nitrate content (RPDcv = 1.63) when new models were developed, taking into account the influence on the robustness of the model exercised by the simultaneity between the NIR and laboratory analyses. Subsequently, using partial least squares discriminant analysis (PLS-DA), the ability of NIRS technology to classify spinach as a function of nitrate content was tested. PLS-DA yielded percentages of correctly classified samples ranging from 73.08-76.92% for the class 'spinach able to be used fresh' to 85.71-73.08% for the class 'preserved, deep-frozen or frozen spinach, both for unbalanced and balanced models respectively, based on NH signal associated with proteins. Overall, the data supports the capability of NIR spectroscopy to establish the final destination of the production of spinach analysed on the plant, as a screening tool for important safety and quality parameters.

Characterizing and authenticating Montilla-Moriles PDO vinegars using near infrared reflectance spectroscopy (NIRS) technology

This study assessed the potential of near infrared (NIR) spectroscopy as a non-destructive method for characterizing Protected Designation of Origin (PDO) “Vinagres de Montilla-Moriles” wine vinegars and for classifying them as a function of the manufacturing process used. Three spectrophotometers were evaluated for this purpose: two monochromator instruments (Foss NIRSystems 6500 SY-I and Foss NIRSystems 6500 SY-II; spectral range 400–2,500 nm in both cases) and a diode-array instrument (Corona 45 VIS/NIR; spectral range 380–1,700 nm). A total of 70 samples were used to predict major chemical quality parameters (total acidity, fixed acidity, volatile acidity, pH, dry extract, ash, acetoin, methanol, total polyphenols, color (tonality and intensity), and alcohol content), and to construct models for the classification of vinegars as a function of the manufacturing method used. The results obtained indicate that this non-invasive technology can be used successfully by the vinegar industry and by PDO regulators for the routine analysis of vinegars in order to authenticate them and to detect potential fraud. Slightly better results were achieved with the two monochromator instruments. The findings also highlight the potential of these NIR instruments for predicting the manufacturing process used, this being of particular value for the industrial authentication of traditional wine vinegars.

Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis

We have developed a multistep strategy that integrates data from several large-scale experiments that suffer from systematic between-experiment variation. This strategy removes such variation that would otherwise mask differences of interest. It was applied to the evaluation of wood chemical analysis of 736 hybrid aspen trees: wild-type controls and transgenic trees potentially involved in wood formation. The trees were grown in four different greenhouse experiments imposing significant variation between experiments. Pyrolysis coupled to gas chromatography/mass spectrometry (Py-GC/MS) was used as a high throughput-screening platform for fingerprinting of wood chemotype. Our proposed strategy includes quality control, outlier detection, gene specific classification, and consensus analysis. The orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to generate the consensus chemotype profiles for each transgenic line. These were thereafter compiled to generate a global dataset. Multivariate analysis and cluster analysis techniques revealed a drastic reduction in between-experiment variation that enabled a global analysis of all transgenic lines from the four independent experiments. Information from in-depth analysis of specific transgenic lines and independent peak identification validated our proposed strategy.

Authentication of organic feed by near-infrared spectroscopy combined with chemometrics: a feasibility study

Organic products tend to retail at a higher price than their conventional counterparts, which makes them susceptible to fraud. In this study we evaluate the application of near-infrared spectroscopy (NIRS) as a rapid, cost-effective method to verify the organic identity of feed for laying hens. For this purpose a total of 36 organic and 60 conventional feed samples from The Netherlands were measured by NIRS. A binary classification model (organic vs conventional feed) was developed using partial least squares discriminant analysis. Models were developed using five different data preprocessing techniques, which were externally validated by a stratified random resampling strategy using 1000 realizations. Spectral regions related to the protein and fat content were among the most important ones for the classification model. The models based on data preprocessed using direct orthogonal signal correction (DOSC), standard normal variate (SNV), and first and second derivatives provided the most successful results in terms of median sensitivity (0.91 in external validation) and median specificity (1.00 for external validation of SNV models and 0.94 for DOSC and first and second derivative models). A previously developed model, which was based on fatty acid fingerprinting of the same set of feed samples, provided a higher sensitivity (1.00). This shows that the NIRS-based approach provides a rapid and low-cost screening tool, whereas the fatty acid fingerprinting model can be used for further confirmation of the organic identity of feed samples for laying hens. These methods provide additional assurance to the administrative controls currently conducted in the organic feed sector.

Dissolution enhancement of a drug exhibiting thermal and acidic decomposition characteristics by fusion processing: a comparative study of hot melt extrusion and KinetiSol dispersing

In this study, hot melt extrusion (HME) and KinetiSol Dispersing (KSD) were utilized to prepare dissolution-enhanced solid dispersions of Roche Research Compound A (ROA), a BCS class II drug. Preformulation characterization studies showed that ROA was chemically unstable at elevated temperatures and acidic pH values. Eudragit L100-55 and AQOAT LF (HPMCAS) were evaluated as carrier polymers. Dispersions were characterized for ROA recovery, crystallinity, homogeneity, and non-sink dissolution. Eudragit L100-55 dispersions prepared by HME required the use of micronized ROA and reduced residence times in order to become substantially amorphous. Compositions containing HPMCAS were also prepared by HME, but an amorphous dispersion could not be obtained. All HME compositions contained ROA-related impurities. KSD was investigated as a method to reduce the decomposition of ROA while rendering compositions amorphous. Substantially amorphous, plasticizer free compositions were processed successfully by KSD with significantly higher ROA recovery values and amorphous character than those achieved by HME. A near-infrared chemical imaging analysis was conducted on the solid dispersions as a measure of homogeneity. A statistical analysis showed similar levels of homogeneity in compositions containing Eudragit L100-55, while differences were observed in those containing HMPCAS. Non-sink dissolution analysis of all compositions showed rapid supersaturation after pH adjustment to approximately two to three times the equilibrium solubility of ROA, which was maintained for at least 24 h. The results of the study demonstrated that KSD is an effective method of forming dissolution-enhanced amorphous solid solutions in cases where HME is not a feasible technique.

Measurement of pesticide residues in peppers by near-infrared reflectance spectroscopy

BACKGROUND

Peppers are a frequent object of food safety alerts in various member states of the European Union owing to the presence in some batches of unauthorised pesticide residues. This study assessed the viability of near-infrared reflectance spectroscopy (NIRS) for the measurement of pesticide residues in peppers. Commercially available spectrophotometers using different sample-presentation methods were evaluated for this purpose: a diode-array spectrometer for intact raw peppers and two scanning monochromators fitted with different sample-presentation accessories (transport and spinning modules) for crushed peppers and for dry extract system for infrared analysis (DESIR), respectively.

RESULTS

Models developed using partial least squares-discriminant analysis (PLS2-DA) correctly classified between 62 and 68% of samples by presence/absence of pesticides, depending on the instrument used. At model validation, the highest percentage of correctly classified samples-75 and 82% for pesticide-free and pesticide-containing samples respectively-were obtained for intact peppers using the diode-array spectrometer.

CONCLUSION

The results obtained confirmed that NIRS technology may be used to provide swift, non-destructive preliminary screening for pesticide residues; suspect samples may then be analysed by other confirmatory analytical methods.

Improving the discriminatory power of a near-infrared microscopy spectral library with a support vector machine classifier

A multi-group classifier based on the support vector machine (SVM) has been developed for use with a library of 48,456 spectra measured by near-infrared reflection microscopy (NIRM) on 227 samples representing 26 animal feed ingredients and 4 possible contaminants of animal origin. The performance of the classifier was assessed by a five-fold cross-validation, dividing at the sample level. Although the overall proportion of misclassifications was 27%, almost all of these involved the confusion of pairs of similar ingredients of vegetable origin. Such confusions are unimportant in the context of the intended use of the library, which is the detection of banned ingredients in animal feed. The error rate in discrimination between permitted and banned ingredients was just 0.17%. The performance of the SVM classifier was substantially better than that of the K-nearest-neighbors method employed in previous work with the same library, for which the comparable error rates are 36% overall and 0.39% for permitted versus banned ingredients.

Multivariate near-infrared reflection spectroscopy strategies for ensuring correct labeling at feed bagging in the animal feed industry

A key concern in animal feed factories is guaranteeing the correct labeling of compound feeds. Therefore, due to incorrect labeling, there is an urgent need for new control methods on the claims that can be made. In this study, this question has been tackled with different multivariate classification algorithms based on the near-infrared spectral fingerprint obtained from a given compound feed analyzed in its original physical market presentation form (i.e., cubes, coarse meals, pellets). The objective of this paper is the evaluation of different methods for establishing a separation among 24 feed types. Two linear methods, soft independent modeling of class analogy (SIMCA) and partial least squares (PLS) with two approaches to classification (PLSD and PLS-LDA); and one nonlinear method, support vector machines (SVM), were studied. The database used had the following structure: a first division was made between granules and meals; within these two groups, there was a second division according to three animal species to which the feed was marketed (bovine, ovine, and porcine); within each species there was a third division according to the age or physiological status of the animal (i.e., lactating dairy cattle, starters, etc.). Given the database structure, all the methods were evaluated following two strategies: (1) development of a model composed of the nine classification models corresponding to the structure of the data; and (2) development of a unique model that discriminates among the 24 classes of different feeds. With both strategies the lowest percentage of misclassified samples was achieved with the SVM method (3.96% with strategy 1 and 2.31% with strategy 2). Among the linear methods evaluated, SIMCA yielded the best results, with a percentage of 8.47% misclassified samples with strategy 1 and 4.05% misclassified samples with strategy 2. The results in this study show the ability of near-infrared spectroscopy to make acceptable classifications of feed types based only on spectral information, with differences in performance depending on the multivariate algorithm used.