Effect of Raman exposure time on the quantitative and discriminant analyses of carotenoid concentrations in intact tomatoes

Chemometrics-powered spectroscopic techniques for the measurement of food-derived phenolics and vitamins in foods: A review

Foods are rich in various bioactive compounds, like phenolics, and vitamins, which play important physiological roles in the human body. The analysis of phenolics and vitamins in plant and animal-based foods is a topic of growing interest. Compared with conventional methods, the chemometrics-powered infrared, Fourier transform-near infrared and mid-infrared, ultraviolet-visible, fluorescence, and Raman spectroscopy offer a reliable, low-cost, and nondestructive means to determine phenolics and vitamins. This study briefly presents the physical properties of phenolics and vitamins and their physiological benefits, features of commonly used spectroscopic techniques, sample preparation for spectroscopic data analysis, and the progress of chemometrics methods for model calibration using spectroscopic data and their primary challenges in predicting phenolics and vitamins in real samples for the last five years. The spectral preprocessing method combined feature extraction quantitative chemometric model comparatively showed the best results for simultaneous and single detection. Finally, this study put forward future directions.

Raman Spectroscopy in the Characterization of Food Carotenoids: Challenges and Prospects

This paper presents an overview of the application of Raman spectroscopy (RS) in characterizing carotenoids, which have recently gained attention due to new findings on their health-promoting effects and rising demand in the food, pharmaceutical, and cosmetic industries. The backbone structure in the form of a polyene chain makes carotenoids sensitive to Raman spectroscopy, mainly due to the stretching vibrations of their conjugated double bonds. Raman spectroscopy is increasingly used in agricultural and food sciences and technologies as it is a non-preparative, environmentally friendly, fast and efficient method for characterizing target analytes. The application of RS in the qualitative and quantitative analysis of carotenoids requires the careful selection and adjustment of various instrument parameters (e.g., laser wavelength, laser power, spectral resolution, detector type, etc.) as well as performing complex chemometric modeling to interpret the Raman spectra. Most of the studies covered in this review focus more on qualitative than quantitative analysis. The most frequently used laser wavelengths are 1064, 785, and 532 nm, while 633 nm is the least used. Considering the sensitivity and complexity of RS, the present study focuses on the specific and critical points in the analysis of carotenoids by RS. The main methodological and experimental principles in the study of food carotenoids by RS are discussed and best practices recommended, while the future prospects and expectations for a wider application of RS, especially in food quality assessment, are emphasized. New Raman techniques such as Spatially Offset Raman Spectroscopy (SORS), Coherent Anti-Stokes Raman Spectroscopy (CARS) and Stimulated Raman Scattering Spectroscopy (SRS), as well as the application of artificial intelligence, are also described in the context of carotenoids analysis.

Technological innovations for reducing tomato loss in the agri-food industry

Food loss and waste has a detrimental impact on the economy, food security and the environment, especially at a time when more than 700 million people around the world suffer from hunger. Fruit and vegetables are highly perishable products and, as a result significant amounts are wasted at various stages of the food supply chain. In addition to this, the hard truth is that a high percentage of fresh produce is wasted because it does not meet quality requirements, mainly in terms of aesthetics. One potential solution to reduce fruit and vegetables losses would be to implement technologies in the industry capable of identifying products that may then be discarded at their destination, which will lead to the search for other commercial strategies, including valorization ones. With this focus, the case of tomato, one of the most produced and consumed commodities worldwide and responsible for high food losses, is addressed in this review. Gas sensors, e-nose systems, acoustic technologies, various spectroscopic techniques, as well as optical imaging techniques, have been proposed for non-invasive monitoring of tomato quality parameters. Indeed, their use has allowed the analysis of CO2, ethylene and aroma components, as gas indicators of the physiological state of the fruit, to detect the presence of surface defects and to estimate, color, firmness, titratable acidity, and the content of total soluble solids and bioactive compounds. However, despite their promising results, most of them have important drawbacks for industrial applications. Among the reviewed technologies, multispectral and hyperspectral analysis are particularly advantageous due to their ability to monitor multiple tomatoes quality parameters and their possibility to operate in online multisensory platforms. The review provides, for the first time to our knowledge, a comprehensive overview of the current scientific efforts and knowledge gaps, as well as future directions of the non-invasive techniques investigated for the analysis of tomato quality parameters. These considerations are crucial for the development of technological solutions to be implemented in the agri-food industry in order to minimize food waste while improving process quality and efficiency, thus contributing to a more circular food system.

Lycopene detection in cherry tomatoes with feature enhancement and data fusion

Lycopene, a biologically active phytochemical with health benefits, is a key quality indicator for cherry tomatoes. While ultraviolet/visible/near-infrared (UV/Vis/NIR) spectroscopy holds promise for large-scale online lycopene detection, capturing its characteristic signals is challenging due to the low lycopene concentration in cherry tomatoes. This study improved the prediction accuracy of lycopene by supplementing spectral data with image information through spectral feature enhancement and spectra-image fusion. The feasibility of using UV/Vis/NIR spectra and image features to predict lycopene content was validated. By enhancing spectral bands corresponding to colors correlated with lycopene, the performance of the spectral model was improved. Additionally, direct spectra-image fusion further enhanced the prediction accuracy, achieving RP2, RMSEP, and RPD as 0.95, 8.96 mg/kg, and 4.25, respectively. Overall, this research offers valuable insights into supplementing spectral data with image information to improve the accuracy of non-destructive lycopene detection, providing practical implications for online fruit quality prediction.

Raman Spectral Feature Enhancement Framework for Complex Multiclassification Tasks

Raman spectroscopy enables label-free clinical diagnosis in a single step. However, identifying an individual carrying a specific disease from people with a multi-disease background is challenging. To address this, we developed a Raman spectral implicit feature augmentation with a Raman Intersection, Union, and Subtraction augmentation strategy (RIUS). RIUS expands the data set without requiring additional labeled data by leveraging set operations at the feature level, significantly enhancing model performance across various applications. On a challenging 30-class bacterial classification task, RIUS demonstrated a substantial improvement, increasing the accuracy of ResNet by 2.1% and that of SE-ResNet by 1.4%, achieving accuracies of 85.7% and 87.1%, respectively, on the Bacteria-ID-4 Data set, where RIUS improved ResNet and SE-ResNet accuracies by 13.6% and 14.5%, respectively, with only ten samples per category. When the sample size was reduced, accuracy gains increased to 31.7% and 38.3%, demonstrating the method's robustness across different sample volumes. Compared to basic augmentation, our method exhibited superior performance across various sample volumes and demonstrated exceptional adaptability to different levels of complexity. RIUS exhibited superior performance, particularly in complex settings. Moreover, cluster analysis validated the effectiveness of the implicit feature augmentation module and the consistency between theoretical design and experimental results. We further validated our approach using clinical serum samples from 70 breast cancer patients and 70 controls, achieving an AUC of 0.94 and a sensitivity of 92.9%. Our approach enhances the potential for precisely identifying diseases in complex settings and offers plug-and-play enhancement for existing classification models.

Rapid, in-situ evaluation of sunflower seed freshness and vigor using Raman microspectroscopy scanning of carotenoids

An ultra-rapid, in-situ Raman microscopy strategy was developed for judging both seed freshness and seed vigor based on relative quantification of carotenoids content during sunflower seed germination. The carotenoids content was determined using the ratio of the Raman peak intensities at 1525 and 1268 cm-1 (I1525/1268). When different samples (harvest times and storage conditions) were soaked in water for 0-24 h, the carotenoids content in the embryonic axes gradually increased, with the carotenoids higher in fresher seeds. Using this method, freshly harvested sunflower seeds (2022) were successfully discriminated from seeds harvested over three previous years (2019-2021) and from seeds subjected to accelerated aging at 45 °C or 60 °C for 2-8 days, the samples were correctly differentiated >90%. In addition, a linear correlation between I1525/1268 ratio and seed germination was found (R2 > 0.95). This proposed method can serve as an ultra-rapid strategy for determination of sunflower seed quality.

Contemporary applications of vibrational spectroscopy in plant stresses and phenotyping

Plant pathogens, including viruses, bacteria, and fungi, cause massive crop losses around the world. Abiotic stresses, such as drought, salinity and nutritional deficiencies are even more detrimental. Timely diagnostics of plant diseases and abiotic stresses can be used to provide site- and doze-specific treatment of plants. In addition to the direct economic impact, this "smart agriculture" can help minimizing the effect of farming on the environment. Mounting evidence demonstrates that vibrational spectroscopy, which includes Raman (RS) and infrared spectroscopies (IR), can be used to detect and identify biotic and abiotic stresses in plants. These findings indicate that RS and IR can be used for in-field surveillance of the plant health. Surface-enhanced RS (SERS) has also been used for direct detection of plant stressors, offering advantages over traditional spectroscopies. Finally, all three of these technologies have applications in phenotyping and studying composition of crops. Such non-invasive, non-destructive, and chemical-free diagnostics is set to revolutionize crop agriculture globally. This review critically discusses the most recent findings of RS-based sensing of biotic and abiotic stresses, as well as the use of RS for nutritional analysis of foods.

Research Progress of Rapid Non-Destructive Detection Technology in the Field of Apple Mold Heart Disease

Apples are rich in vitamins and dietary fiber and are one of the essential fruits in people's daily diet. China has always been a big apple consumer, and with the improvement of people's life quality, nutrition, and health requirements, the demand for high-quality apples has increased year by year. Apple mold heart disease is one of the main diseases affecting apple quality. However, this disease cannot be easily detected from the surface, so it is difficult to detect mold heart disease. Therefore, this paper focuses on the analysis of seven non-destructive detection technologies, including near infrared spectroscopy technology, hyperspectral technology, Raman spectroscopy technology, electronic nose technology, acoustic technology, electrical technology, and magnetic technology, summarizes their application status in the detection of apple mold heart disease, and then analyzes their advantages and disadvantages. Combined with the current rapid development of artificial intelligence (AI) technology, this paper proposes the future development trends of using non-destructive technologies to detect apple mold heart disease. It is expected to provide basic theory and application references for the intelligent detection of apple mold heart disease.

Interaction mechanism of carotenoids and polyphenols in mango peels

In this study, carotenoids and polyphenols were demonstrated to be the major active substances in the crude pigment extracts (CPE) of mango peels, accounting for 0.26 mg/g and 0.15 mg/g, respectively. The interactions between carotenoids and polyphenols in CPE was observed, as evidenced by that polyphenols significantly improved the antioxidant activity and storage stability of carotenoids in the CPE. Meanwhile, scanning electron microscopy showed that polyphenols are tightly bound to carotenoids. To further elucidate the interaction mechanism, the monomers of carotenoids and polyphenols were identified by HPLC and LC-MS analysis. Lutein (203.85 μg/g), β-carotene (41.40 μg/g), zeaxanthin (4.20 μg/g) and α-carotene (1.50 μg/g) were authenticated as the primary monomers of carotenoids. Polyphenols were mainly consisted of gallic acid (95.10 μg/g), quercetin-3-β-glucoside (29.10 μg/g), catechin (11.85 μg/g) and quercetin (11.55 μg/g). The interaction indexes between carotenoid and polyphenol monomer of CPE were calculated. The result indicated that lutein and gallic acid showed the greatest synergistic effect on the scavenging of DPPH and ABTS radical, suggesting the interaction between carotenoids and polyphenols in CPE was mainly caused by lutein and gallic acid. Molecular dynamics simulations and thermodynamic parameters analysis demonstrated that hydrogen bonding, electrostatic interactions, and van der Waals forces played dominant roles in the interaction between lutein and gallic acid, which was confirmed by Raman and X-ray diffraction. These results provided a new perspective on the interaction mechanism between carotenoids and polyphenols, which offered a novel strategy for the enhancement of the activities and stability of bioactive substances.

Rationalizing In Situ Active Repair in Hydrogen Evolution Photocatalysis via Non-Invasive Raman Spectroscopy

Currently, most photosensitizers and catalysts used in the field of artificial photosynthesis are still based on rare earth metals and should thus be utilized as efficiently and economically as possible. While repair of an inactivated catalyst is a potential mitigation strategy, this remains a challenge. State-of-the-art methods are crucial for characterizing reaction products during photocatalysis and repair, and are currently based on invasive analysis techniques limiting real-time access to the involved mechanisms. Herein, we use an innovative in situ technique for detecting both initially evolved hydrogen and after active repair via advanced non-invasive rotational Raman spectroscopy. This facilitates unprecedently accurate monitoring of gaseous reaction products and insight into the mechanism of active repair during light-driven catalysis enabling the identification of relevant mechanistic details along with innovative repair strategies.

Using optimized particle imaging of micro-Raman to characterize microplastics in water samples

Characterizing the chemical properties, morphologies, size, and quantities of microplastics (MPs) in water samples with high precision is critically important for understanding the environmental behaviors of MPs. Traditional detection methods, such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy point-by-point detection, provide worthy reference techniques but are time- and labor-consuming. We established a super time-saving and high-precision technique to characterize MPs using micro-Raman automatic particle identification (MR-API). Based on the identification of PS spheres, screen magnification, exposure time, and the number of scans are selected as crucial detection parameters for MR-API analysis, which highly affect the precision of the results. Detecting particles down to 1 μm requires magnification of the mosaic until the scale showed 200 μm. The recommended setting parameters were 83.33 or 100 ms exposure time, 20 scans, 7 mW laser power, and 1 μm image pixel size, suitable for polystyrene (PS), polypropylene (PP), polyethylene terephthalate (PET), polyethylene (PE), polyvinyl chloride (PVC), and polyamide (PA) particles detection. With the complete procedure of MR-API measurements, the recovery of MPs was 61.67-90.00 %. To validate the feasibility of the MR-API, the method was used to detect samples of known plastic types (mask leachates) and unknown plastic types (urban lake). A total of 4540 particles in the sample of mask leachates consuming 35 h 50 min 43 s, and 0.92 ± 0.49 % of particles were identified as MPs. The urban river sample efficiently identified PP, PET, PE, PVC, PS, EVA, and VC/VAC MPs using this method. The detected MPs size ranged from 8.3 to 5000 μm, saving 75.03 % and 58.38 % of the time compared to the conventional micro-FTIR and micro-Raman point-by-point methods, respectively. Therefore, this method is effective for detecting MPs in the environmental samples and has excellent prospects.

Raman spectroscopy in crop quality assessment: focusing on sensing secondary metabolites: a review

As a crop quality sensor, Raman spectroscopy has been consistently proposed as one of the most promising and non-destructive methods for qualitative and quantitative analysis of plant substances, because it can measure molecular structures in a short time without requiring pretreatment along with simple usage. The sensitivity of the Raman spectrum to target chemicals depends largely on the wavelength, intensity of the laser power, and exposure time. Especially for plant samples, it is very likely that the peak of the target material is covered by strong fluorescence effects. Therefore, methods using lasers with low energy causing less fluorescence, such as 785 nm or near-infrared, are vigorously discussed. Furthermore, advanced techniques for obtaining more sensitive and clear spectra, like surface-enhanced Raman spectroscopy, time-gated Raman spectroscopy or combination with thin-layer chromatography, are being investigated. Numerous interpretations of plant quality can be represented not only by the measurement conditions but also by the spectral analysis methods. Up to date, there have been attempted to optimize and generalize analysis methods. This review summarizes the state of the art of micro-Raman spectroscopy in crop quality assessment focusing on secondary metabolites, from in vitro to in vivo and even in situ, and suggests future research to achieve universal application.