A review on the application of vibrational spectroscopy to the chemistry of nuts

Diversity of flavonoids in five Torreya grandis cultivars: Integrating metabolome and transcriptome to elucidate potential applications for health and metabolic engineering

Torreya grandis is a medicinally and nutritionally rich tree nut with high flavonoid content. However, a thorough evaluation of the variation in flavonoids among T. grandis cultivars remains to explore. In this study, we conducted a widely-targeted metabolomic analysis of five T. grandis cultivars, identifying 64 distinct flavonoids. Key subclasses of flavonoids, including flavan-3-ols, anthocyanidins, procyanidins, and flavonols, were characterized for their abundance and related to their potential health benefits. Our analysis revealed that T. grandis 'Shishengfei' exhibited the highest flavonoid diversity and content, while other cultivars showed relatively lower levels. By integrating transcriptome data, we identified genes and metabolic pathways associated with flavonoid biosynthesis, which could offer potential targets for metabolic engineering to enhance the flavonoid content in T. grandis. This research not only establishes a database of flavonoid components in T. grandis but also offers insights for selecting and breeding cultivars with enhanced health-promoting properties, contributing to the fields of food chemistry and nutrition.

NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review

Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.

Vegetable oils: Classification, quality analysis, nutritional value and lipidomics applications

Lipids are widespread in nature and play a pivotal role as a source of energy and nutrition for the human body. Vegetable oils (VOs) constitute a significant category in the food industry, containing various lipid components that have garnered attention for being natural, environmentally friendly and health-promoting. The review presented the classification of raw materials (RMs) from oil crops and quality analysis techniques of VOs, with the aim of improving comprehension and facilitating in-depth research of VOs. Brief descriptions were provided for four categories of VOs, and quality analysis techniques for both RMs and VOs were generalized. Furthermore, this study discussed the applications of lipidomics technology in component analysis, processing and utilization, quality determination, as well as nutritional function assessment of VOs. Through reviewing RMs and quality analysis techniques of VOs, this study aims to encourage further refinement and development in the processing and utilization of VOs, offering valuable references for theoretical and applied research in food chemistry and food science.

Rapid and non-destructive cinnamon authentication by NIR-hyperspectral imaging and classification chemometrics tools

Cinnamon is a valuable aromatic spice widely used in pharmaceutical and food industry. Commonly, two-cinnamon species are available in the market, Cinnamomum verum (true cinnamon), cropped only in Sri Lanka, and Cinnamomum cassia (false cinnamon), cropped in different geographical origins. Thus, this work aimed to develop classification models based on NIR-hyperspectral imaging (NIR-HSI) coupled to chemometrics to classify C. verum and C. cassia sticks. First, principal component analysis (PCA) was applied to explore hyperspectral images. Scores surface displayed the high similarity between species supported by comparable macronutrient concentration. PC3 allowed better class differentiation compared to PC1 and PC2, with loadings exhibiting peaks related to phenolics/aromatics compounds, such as coumarin (C. cassia) or catechin (C. verum). Partial least square discriminant analysis (PLS-DA) and Support vector machine (SVM) reached similar performance to classify samples according to origin, with error = 3.3 % and accuracy = 96.7 %. A permutation test with p < 0.05 validated PLS-DA predictions have real spectral data dependency, and they are not result of chance. Pixel-wise (approach A) and sample-wise (approach B, C and D) classification maps reached a correct classification rate (CCR) of 98.3 % for C. verum and 100 % for C. cassia. NIR-HSI supported by classification chemometrics tools can be used as reliable analytical method for cinnamon authentication.

New research development on trans fatty acids in food: Biological effects, analytical methods, formation mechanism, and mitigating measures

The trans fatty acids (TFAs) in food are mainly generated from the ruminant animals (meat and milk) and processed oil or oil products. Excessive intake of TFAs (>1% of total energy intake) caused more than 500,000 deaths from coronary heart disease and increased heart disease risk by 21% and mortality by 28% around the world annually, which will be eliminated in industrially-produced trans fat from the global food supply by 2023. Herein, we aim to provide a comprehensive overview of the biological effects, analytical methods, formation and mitigation measures of TFAs in food. Especially, the research progress on the rapid, easy-to-use, and newly validated analytical methods, new formation mechanism, kinetics, possible mitigation mechanism, and new or improved mitigation measures are highlighted. We also offer perspectives on the challenges, opportunities, and new directions for future development, which will contribute to the advances in TFAs research.

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

This study uses Raman and IR spectroscopic methods for the detection of adulterants in marine oils. These techniques are used individually and as low-level fused spectroscopic data sets. We used cod liver oil (CLO) and salmon oil (SO) as the valuable marine oils mixed with common adulterants, such as palm oil (PO), omega-3 concentrates in ethyl ester form (O3C), and generic fish oil (FO). We showed that support vector machines (SVM) can classify the adulterant present in both CLO and SO samples. Furthermore, partial least squares regression (PLSR) may be used to quantify the adulterants present. For example, PO and O3C adulterated samples could be detected with a RMSEP value less than 4%. However, the FO adulterant was more difficult to quantify because of its compositional similarity to CLO and SO. In general, data fusion improved the RMSEP for PO and O3C detection. This shows that Raman and IR spectroscopy can be used in concert to provide a useful analytical test for common adulterants in CLO and SO.

Assessment of Total Fat and Fatty Acids in Walnuts Using Near-Infrared Hyperspectral Imaging

"Persian" walnut (Juglans Regia L.) is one of the most consumed tree nuts in the world. It is rich in several bioactive compounds, with polyunsaturated and monounsaturated fatty acids (PUFA and MUFA) appearing at high concentrations. Walnut consumption protects against cardiovascular, carcinogenic, and neurological disorders. The fatty acid profile has usually been determined by gas chromatography, a reliable and robust tool, but also complex, polluting, and time consuming. In this study, near infrared hyperspectral imaging has been used for the screening of total fat, MUFA, PUFA, saturated, and individual fatty acids in walnuts. Five different walnuts varieties have been considered and modified partial least square (MPLS) regressions have been performed. The SEs of prediction (SEP) in external validation (ranged from 2.12% for PUFA to 13.08% for MUFA) suggest that hyperspectral imaging can be a reliable tool for controlling these parameters in a simple, non-destructive and environmentally friendly way.

Authentication of the Botanical and Geographical Origin and Detection of Adulteration of Olive Oil Using Gas Chromatography, Infrared and Raman Spectroscopy Techniques: A Review

Olive oil is among the most popular supplements of the Mediterranean diet due to its high nutritional value. However, at the same time, because of economical purposes, it is also one of the products most subjected to adulteration. As a result, authenticity is an important issue of concern among authorities. Many analytical techniques, able to detect adulteration of olive oil, to identify its geographical and botanical origin and consequently guarantee its quality and authenticity, have been developed. This review paper discusses the use of infrared and Raman spectroscopy as candidate tools to examine the authenticity of olive oils. It also considers the volatile fraction as a marker to distinguish between different varieties and adulterated olive oils, using SPME combined with gas chromatography technique.

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

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.

Terahertz spectroscopy technology as an innovative technique for food: Current state-of-the-Art research advances

With the dramatic development of source and detector components, terahertz (THz) spectroscopy technology has recently shown a renaissance in various fields such as medical, material, biosensing and pharmaceutical industry. As a rapid and noninvasive technology, it has been extensively exploited to evaluate food quality and ensure food safety. In this review, the principles and processes of THz spectroscopy are first discussed. The current state-of-the-art applications of THz and imaging technologies focused on foodstuffs are then discussed. The advantages and challenges are also covered. This review offers detailed information for recent efforts dedicated to THz for monitoring the quality and safety of various food commodities and the feasibility of its widespread application. THz technology, as an emerging and unique method, is potentially applied for detecting food processing and maintaining quality and safety.

Comparative review and the recent progress in detection technologies of meat product adulteration

Meat adulteration, mainly for the purpose of economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat adulteration. Considering the importance and rapid advances in meat adulteration detection technologies, a comprehensive review to summarize the recent progress in this area and to suggest directions for future progress is beneficial. In this review, destructive meat adulteration technologies based on DNA, protein, and metabolite analyses and nondestructive technologies based on spectroscopy were comparatively analyzed. The advantages and disadvantages, application situations of these technologies were discussed. In the future, determining suitable indicators or markers is particularly important for destructive methods. To improve sensitivity and save time, new interdisciplinary technologies, such as biochips and biosensors, are promising for application in the future. For nondestructive techniques, convenient and effective chemometric models are crucial, and the development of portable devices based on these technologies for onsite monitoring is a future trend. Moreover, omics technologies, especially proteomics, are important methods in laboratory detection because they enable multispecies detection and unknown target screening by using mass spectrometry databases.

Detection of Aflatoxin B1 Based on a Porous Anodized Aluminum Membrane Combined with Surface-Enhanced Raman Scattering Spectroscopy

An Aflatoxin B₁ (AFB₁) biosensor was fabricated via an Ag nanoparticles assembly on the surface of a porous anodized aluminum (PAA) membrane. First, the Raman reporter 4-Aminothiophenol (4-ATP) and DNA (partially complementary to AFB₁ aptamer) were attached to the surface of Ag nanoparticles (AgNPs) by chemical bonding to form a 4-ATP-AgNPs-DNA complex. Similarly, the surface of a PAA membrane was functionalized with an AFB₁ aptamer. Then, the PAA surface was functionalized with 4-ATP-AgNPs-DNA through base complementary pairing to form AgNPs-PAA sensor with a strong Raman signal. When AFB₁ was added, AgNPs would be detached from the PAA surface because of the specific binding between AFB₁ and the aptamer, resulting in a reduction in Raman signals. The detection limit of the proposed biosensor is 0.009 ng/mL in actual walnut and the linear range is 0.01-10 ng/mL. The sensor has good selectivity and repeatability; it can be applied to the rapid qualitative and quantitative detection of AFB₁.

Comparative study on the use of three different near infrared spectroscopy recording methodologies for varietal discrimination of walnuts

Walnut fruit (Juglans regia L.) is an internationally well-known product with an important tradition of consumption. Its health benefits and economic importance in the food industry make this nut an interesting research topic. In this feasibility study, 200 walnut samples of 5 different varieties were collected and their near infrared (NIR) spectra were recorded with 3 different devices: a benchtop Fourier transform near infrared (FT-NIR) spectrograph, a dispersive hyperspectral imaging camera and a portable NIR dispersive spectrograph. Discriminant analyses were applied and different methods for the varietal discrimination of walnuts were obtained and compared. Up to 96 and 84% of correct identification were respectively obtained in internal (training set) and external validations. Better results were obtained covering the entire shell surface than collecting a unique random spectrum per sample. Moreover, FT-NIR and hyperspectral tools produced classification models with a lower classification error in internal and external validations than the portable NIR one.

Fusion of Near-Infrared and Raman Spectroscopy for In-Line Measurement of Component Content of Molten Polymer Blends

Spectral measurement techniques, such as the near-infrared (NIR) and Raman spectroscopy, have been intensively researched. Nevertheless, even today, these techniques are still sparsely applied in industry due to their unpredictable and unstable measurements. This paper put forward two data fusion strategies (low-level and mid-level fusion) for combining the NIR and Raman spectra to generate fusion spectra or fusion characteristics in order to improve the in-line measurement precision of component content of molten polymer blends. Subsequently, the fusion value was applied to modeling. For evaluating the response of different models to data fusion strategy, partial least squares (PLS) regression, artificial neural network (ANN), and extreme learning machine (ELM) were applied to the modeling of four kinds of spectral data (NIR, Raman, low-level fused data, and mid-level fused data). A system simultaneously acquiring in-line NIR and Raman spectra was built, and the polypropylene/polystyrene (PP/PS) blends, which had different grades and covered different compounding percentages of PP, were prepared for use as a case study. The results show that data fusion strategies improve the ANN and ELM model. In particular, mid-level fusion enables the in-line measurement of component content of molten polymer blends to become more accurate and robust.