A novel FT-IR spectroscopic method based on lipid characteristics for qualitative and quantitative analysis of animal-derived feedstuff adulterated with ruminant ingredients

A Study of the Reliability and Accuracy of the Real-Time Detection of Forage Maize Quality Using a Home-Built Near-Infrared Spectrometer

The current study was conducted to explore the real-time detection capability of a home-built grating-type near-infrared (NIR) spectroscopy online system to determine forage maize quality. The factor parameters affecting the online NIR spectrum collection were analyzed, and the results indicated that the detection optical path of 12 cm, conveyor speeds of 10 cm s−1, and number of scans of 32 were the optimal parameters. Choosing the crude protein and moisture of forage maize as quality indicators, the reliability of the home-built NIR online spectrometer was confirmed compared with other general research NIR instruments. In addition, an NIR online multivariate analysis model developed using the partial least squares (PLS) method for the prediction of forage maize quality was established, and the reliability, applicability, and stability of the NIR model were further discussed. The results illustrated that the home-built grating-type NIR online system performed satisfying and comparable accuracy and repeatability of the real-time prediction of forage maize quality.

A novel analytical strategy for discriminating antibiotic mycelial residue adulteration in feed based on ATR-IR and microscopic infrared imaging

The Antibiotic mycelial residue (AMR) contains antibiotic residue, there are safety risks if it is used illegally in feed. This study investigated the feasibility of qualitative identification of AMR in protein feed and self-prepared feed based on attenuated total reflection mid-infrared spectrum (ATR-IR) and microscopic infrared imaging. Cottonseed meal (CM), soybean meal (SM), distillers dried grains with solubles (DDGS), nucleotide residue (NR), oxytetracycline residue (OR) and streptomycin sulfate residue (SR) and two self-prepared feed (broiler and pig) were used as research objects. The results showed that there were characteristic peaks at 1614 cm^-1, 1315 cm^-1, 779 cm^-1, 514 cm^-1 in the ATR-IR spectra of AMR, which were related to calcium oxalate hydrate. After detection, the content of total calcium and calcium oxalate in AMR were higher than those in protein feed. ATR-IR can quickly realize the qualitative discrimination of pure material samples. The combination of ATR-IR and partial least squares discriminant analysis (PLSDA) was effective in discriminating AMR from CM and SM with a single component (the classification errors were 0), but it cannot meet the discrimination of AMR from the fermented protein feed (such as DDGS and NR, the classification errors were 0.10 and 0.12) and self-prepared feed with complex components. Compared with ATR-IR, microscopic infrared imaging was less affected by the sample complexity. Multi-component samples belong to physical mixing and will not affect the infrared spectra of each component. Therefore, microscopic infrared imaging combined with effective information extraction algorithms such as cosine similarity can distinguish OR in the fermented protein feed and self-prepared feed. The above results showed that the advantages of ATR-IR and microscopic infrared imaging were complementary, which provided a new idea for the discrimination analysis of illegal feed additives.

Complementarity of FT-IR and Raman spectroscopies for the species discrimination of meat and bone meals related to lipid molecular profiles

The objective of this study is to realise the successful species discrimination of meat and bone meals (MBMs) based on the complementarity of FT-IR and Raman spectra. The spectral variation of typical lipid profiles on FT-IR and Raman spectra of MBMs as well as the chemical structure-related principle of FT-IR and Raman spectroscopies related to lipid characteristics were investigated. Lipids from MBMs were separately collected by FT-IR and Raman spectroscopes, which illustrated both spectra (1800 ~ 900 cm^-1) presented different typical lipid peaks. The combination of FT-IR and Raman spectra contributed to establish the more reliable and robust species discrimination model compared to single FT-IR or Raman spectra due to more detailed and integrated molecular vibration information. Degree of unsaturation and cis/trans fatty acid contents were considered the important chemical structure-related factors for ideal species discrimination. Complementation of FT-IR and Raman spectra performed synergistic enhancement to the species discrimination with diverse contributions.

A novel near infrared spectroscopy analytical strategy for meat and bone meal species discrimination based on the insight of fraction composition complexity

This study analyzed the meat and bone meal (MBM) matrix complexity from the perspective of fraction composition diversity and a classification strategy was proposed to accurately and rapidly identify the MBM species based on near infrared spectroscopy (NIRS). Partial Least Squares-Discrimination Analysis (PLS-DA) based on full samples, meat meal (MM), MBM and bone meal (BM) performed with decreasing classification errors of 0.115, 0.079, 0.044 and 0.039 which were partly caused by wide sample range; bone fraction content had positive correlation with most of MBM species differences reflected by principal component scores; and PLS-DA classification errors among MM, MBM and BM were lower than 0.013. To take fully advantage of the above results, a sequential classification strategy was proposed; near infrared spectra were selected (belong to MM, MBM or BM) and then species discrimination analysis was conducted based on the specific PLS-DA model.

FT-IR-based quantitative analysis strategy for target adulterant in fish oil multiply adulterated with terrestrial animal lipid

The interference of nontarget adulterant on FT-IR-based target adulterant quantitative analysis was explored and a sequential strategy was proposed to improve the prediction accuracy of the quantitative analysis model. Based on the FT-IR data of fish oil adulterated with terrestrial animal lipid, PLS and PLS-DA results show that quantitative analysis modeled by multiple and single adulteration data do not apply to each other; quantitative models based on the fusion of single and multiple adulteration data were established and showed a low quantitative analysis precision (higher RSD); and the sensitivity and specificity of discrimination analysis for multiply and singly adulterated fish oils both all exceed 0.910. To enhance the detection accuracy, a sequential strategy was proposed; identifying singly or multiply adulterated fish oil and then quantifying the content of adulterant was considered an efficient approach.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Omics Forecasting: Predictive Calculations Permit the Rapid Interpretation of High-Resolution Mass Spectral Data from Complex Mixtures

For some complex mixtures, chromatographic techniques are insufficient to separate the large numbers of compounds present. In addition, these mixtures often contain compounds with similar or identical molecular masses and shared fragmentation transitions. Advancements in mass spectrometry have provided more and more detailed molecular profiles with significant increases in resolution. This has led to a capacity to distinguish a very large number of compounds in complex mixtures, providing overwhelming data sets. The approach of calculating molecular formulas from a mass list has become more and more problematic as the number of signals has increased exponentially, to the point that it has become impossible to manually interpret the thousands of mass signals. The current approach is to calculate a list of possible formulas that fall within a specific mass error of the observed signal. Then, one must look for possible structures that can be derived from each entry on the list of formulas. However, an alternative approach is to anticipate the possible structures of a particular set of compounds, such as red wine pigments, and then compare the ion signals against a predicted list. To that end, starting with known wine pigment types, we have generated a set of expected wine pigment variants based on known derivatives of condensed tannin oligomers, anthocyanins, and fermentation products. After the ability to distinguish compounds by mass spectrometry was accounted for, over 1 million results were generated consisting of known and anticipated wine pigments. A comparison with a small sample of wine phenolic fractions show a large number of matches, suggesting that this approach may be helpful.

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.