Estimating the sensory qualities of tomatoes using visible and near-infrared spectroscopy and interpretation based on gas chromatography-mass spectrometry metabolomics

Non-contact interactance NIR spectroscopy for estimating TSS and sensory sweetness in conveyor-belt transported cherry tomatoes (Lycopersicon esculentum 'Piccolo')

Sweetness in tomatoes is a crucial quality feature, especially for high-cost varieties. Without efficient measurements, producers cannot guarantee sweetness, which may lead to low repurchase rates and increased food waste. In this article we report how total soluble solids (TSS), a measure for total sugar content, can be measured in cherry tomatoes using non-contact near-infrared (NIR) interactance spectroscopy. A prototype system working in the wavelength range 760-1080 nm was tested and compared with a commercial handheld NIR reflection instrument operating in the range of 909-1676 nm. The instruments were calibrated using data collected from 200 tomatoes and tested on 5 independent sets harvested over the next 14 months, a total of 611 single tomatoes and 22 tomato trusses. TSS for single tomatoes was in the range 5.3-14 %. NIR interaction obtained typical standard errors of prediction in the range 0.38-0.52 %, significantly lower (p < 0.05) compared to NIR reflection (0.70-0.98 %). Approximately the same accuracy was obtained by interaction measurements on tomatoes moving on a conveyor belt, both in lab (0.47-0.53 %) and in industry (0.39 %). Whole tomato trusses were measured in movement, and TSS could successfully be measured in each tomato on the truss. Three of the test sets were subjected to sensory analysis performed by a trained panel of 10 assessors, revealing high correlations (0.88-0.99) between sensory sweetness and NIR estimated TSS. The results demonstrate that a carefully designed NIR interaction instrument can be used for rapid and non-contact monitoring and sorting of cherry tomatoes according to sweetness.

Gas chromatography-mass spectrometry metabolic profiling and sensory evaluation of greenhouse mangoes (Mangifera indica L. 'Irwin') over multiple harvest seasons

Compared to outdoor mango cultivation in the tropics, greenhouse cultivation in temperate regions is less reported due to its short history and small scale. Here, we evaluated for the first time the taste-focused quality of greenhouse-grown mangoes (Irwin) by GC-MS metabolic profiling and sensory evaluation for over three years (2021-2023). The relative standard deviation in sensory evaluation scores was approximately 15 % each year. Meanwhile, principal component analysis was performed on 45 identified metabolites and clustered with similar topology over three years. Orthogonal partial least squares regression analysis showed that a prediction model could be constructed with R2, Q2 = 0.9 or higher for all three years. Furthermore, the 2021 prediction model was used to evaluate the accuracy of sensory evaluation scores in a different year (2023). Compared to the model that used all 45 metabolites as explanatory variables, the accuracy of the model improved when using only 24 important metabolites which are common to both years (2021 and 2022), suggesting that these metabolites are highly reproducible across years. This study would contribute not only to fundamental greenhouse information, but also to the improvement of quality and cultivation methods in greenhouse in the future.

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.

Metabolomic profiling reveals taste development in tomatoes during cold storage with vacuum packaging

The alteration in the taste of tomatoes after storage significantly influences consumer demand. Identification of the key compounds and related metabolic pathways involved in the taste changes of tomatoes during storage is very important for the tomato industry. This study examined the alterations in the taste of tomatoes stored at 4 °C under vacuum packaging, focusing on changes in metabolites and identifying specific metabolites that serve as markers for different taste attributes. The results showed that the decrease in sourness after storage at low temperatures was strongly associated with the decline in the function of the metabolic pathways of acetyl phosphate, pyrrole-2-carboxylic acid, and hydroxypyruvate. The increase in umami was closely related to the enrichment of amino acid-related metabolites such as tryptophan and proline. Furthermore, the utilization of vacuum packaging exerted an influence on the bitterness and astringency of tomatoes through altering the production of metabolites such as dimethylglycine, betaine, and flavonoids. These findings are essential for comprehending the variability in tomato taste and directing future quality control.

Analysis of volatile flavor compounds in Antarctic krill paste with different processing methods based on GC-IMS

In this study, shrimp paste was prepared using Antarctic krill and fermented Antarctic krill shrimp paste as raw materials. Two commonly used heating methods, stir-fried and steaming, were analyzed, the main difference between the two methods being that stir-frying involves putting the shrimp paste into a wok and stir-frying it for different periods of time, while steaming involves putting the shrimp paste into a steamer and steaming it for different periods of time. The effects of different salt concentrations and processing techniques on the volatile flavor compounds of shrimp paste were also observed. Electronic nose and gas chromatography-ion mobility spectrometry (GC-IMS) were employed to analyze the volatile flavor compounds. A total of 52 volatile flavor compounds were detected by GC-IMS, of which 38 were identified (including monomers, dimers, and polymers). The identified compounds included 11 aldehydes, 6 ketones, 14 alcohols, 2 esters, 2 acids, 1 pyridine compound, and 2 sulfur compounds. In addition, 14 compounds were identifiable. Using the results of the electronic nose analysis, we were also able to differentiate between the volatile flavor compounds in shrimp pastes produced by different processing methods.

Estimating the sensory-associated metabolites profiling of matcha based on PDO attributes as elucidated by NIRS and MS approaches

Matcha has been globally valued by consumers for its distinctive fragrance and flavor since ancient times. Currently, the protected designation of origin (PDO) certified matcha, characterized by unique sensory attributes, has garnered renewed interest from consumers and the industry. Given the challenges associated with assessing sensory perceptions, the origin of PDO-certified matcha samples from Guizhou was determined using NIRS and LC-MS platforms. Notably, the accuracy of our established attribute models, based on informative wavelengths selected by the CARS-PLS method, exceeds 0.9 for five sensory attributes, particularly the particle homogeneity attribute (with a validation correlation coefficient of 0.9668). Moreover, an LC-MS method was utilized to analyze non-target matcha metabolites to identify the primary flavor compounds associated with each flavor attribute and to pinpoint the key constituents responsible for variations in grade and flavor intensity. Additionally, high three-way intercorrelations between descriptive sensory attributes, metabolites, and the selected informative wavelengths were observed through network analysis, with correlation coefficients calculated to quantify these relationships. In this research, the integration of matcha chemical composition and sensory panel data was utilized to develop predictive models for assessing the flavor profile of matcha based on its chemical properties.

Uncovering the Role of Hormones in Enhancing Antioxidant Defense Systems in Stressed Tomato (Solanum lycopersicum) Plants

Tomato is one of the most important fruits worldwide. It is widely consumed due to its sensory and nutritional attributes. However, like many other industrial crops, it is affected by biotic and abiotic stress factors, reducing its metabolic and physiological processes. Tomato plants possess different mechanisms of stress responses in which hormones have a pivotal role. They are responsible for a complex signaling network, where the antioxidant system (enzymatic and non-enzymatic antioxidants) is crucial for avoiding the excessive damage caused by stress factors. In this sense, it seems that hormones such as ethylene, auxins, brassinosteroids, and salicylic, jasmonic, abscisic, and gibberellic acids, play important roles in increasing antioxidant system and reducing oxidative damage caused by different stressors. Although several studies have been conducted on the stress factors, hormones, and primary metabolites of tomato plants, the effect of endogenous and/or exogenous hormones on the secondary metabolism is still poorly studied, which is paramount for tomato growing management and secondary metabolites production. Thus, this review offers an updated overview of both endogenous biosynthesis and exogenous hormone application in the antioxidant system of tomato plants as a response to biotic and abiotic stress factors.

Non-Destructive Quality Evaluation of 80 Tomato Varieties Using Vis-NIR Spectroscopy

Traditional biochemical methods are resource- and time-consuming; therefore, there is a need for cost-effective alternatives. A spectral analysis is one of the non-destructive techniques that are more widely used for fruit quality determination; however, references are needed for traditional methods. In this study, visible and near-infrared (Vis-NIR) spectroscopy was used to analyze the internal quality attributes of tomatoes. For the first time, 80 varieties with large differences in fruit size, shape, color, and internal structure were used for an analysis. The aim of this study was to develop models suitable to predict a taste index, as well as the content of lycopene, flavonoids, β-carotene, total phenols, and dry matter of intact tomatoes based on Vis-NIR reflectance spectra. The content of phytochemicals was determined in 80 varieties of tomatoes. A total of 140 Vis-NIR reflectance spectra were obtained using the portable spectroradiometer RS-3500 (Spectral Evolution Inc.). Partial least squares regression (PLS) and multiple scatter correction (MSC) were used to develop calibration models. Our results indicated that PLS models with good prediction accuracies were obtained. The present study showed the high capability of Vis-NIR spectroscopy to determine the content of lycopene and dry matter of intact tomatoes with a determination coefficient of 0.90 for both parameters. A regression fit of R2 = 0.86, R2 = 0.84, R2 = 0.82, and R2= 0.73 was also achieved for the taste index, flavonoids, β-carotene, and total phenols, respectively.

Gel Biter: food texture discriminator based on physical reservoir computing with multiple soft materials

The human oral structure contains organs with distinctly different physical properties, such as teeth, gums, and tongues. When food enters the oral cavity, we can recognize the tactile sensation and shape of the object from multiple perspectives through the texture of the teeth and tongue. Therefore, it is possible to regard oral structures as a group of tactile sensors based on these functions. In this study, we developed a soft-matter artificial mouth that can accurately detect subtle differences in texture by creating and combining oral structural organs using polymer materials with different physical properties and mounting them as end-effectors for a robot arm. The same piezoelectric film sensor was embedded inside each organ, making it possible to acquire tactile sensations from the same object as completely different signal waveforms. We tested whether the sensor data obtained from each soft-matter material could be used for excellent object recognition by applying various machine learning methods. In an actual experiment, we learned the waveform data obtained from chewing sweets and snacks, such as rice crackers, and applied machine learning to classify the data, which led to an accuracy rate of over 90%.

A new quantitative index for the assessment of tomato quality using Vis-NIR hyperspectral imaging

The quality of tomatoes is usually predicted by measuring a single index, rather than a comprehensive index. To find a comprehensive index, visible and near infrared (Vis-NIR) hyperspectral imaging was used for capturing the images of three varieties of tomatoes, and twelve quality indexes were measured as the reference standards. The changing trends and correlations of different indexes were analyzed, and comprehensive quality index (CQI) was proposed through factor analysis. The characteristic wavelengths were selected by successive projection algorithm (SPA) based on the hyperspectral data, which was used to establish three regression models for CQI prediction. The result indicated that MLR achieved good performance withR_V² = 0.87, RMSEV = 1.33 and RPD = 2.58. After that, spatial distribution map was generated to visualize the CQI in tomato fruit. This study indicated that the comprehensive quality of tomatoes can be predicted non-destructively based on hyperspectral imaging and chemometrics, determining the optimal harvesting period.

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

Estimation of komatsuna freshness using visible and near-infrared spectroscopy based on the interpretation of NMR metabolomics analysis

The objective of this study was to explore the potentiality and mechanism of visible and near-infrared (Vis-NIR) spectroscopy in estimating the freshness of komatsuna. We monitored the cumulative CO₂ production of komatsuna stored under different conditions as a freshness indicator and measured the Vis-NIR spectra of komatsuna as the predictor. Using the informative wavelengths (IW) selected using the stepwise selectivity ratio method, we constructed an accurate freshness prediction model through PLSR analysis. The IW in the visible region were attributed to pigments such as chlorophyll. In the NIR region, ten amino acids were identified as directly or indirectly contributing to the IW and were highly related to freshness. They were confirmed on the basis of the strong correlations between the informative NIR signals and NMR signals, which were determined using statistical heterospectroscopy. The results demonstrate the feasibility of Vis-NIR spectroscopy in estimating the freshness of komatsuna using the IW.

Instrumental Texture Profile of Traditional Varieties of Tomato (Solanum lycopersicum L.) and its Relationship to Consumer Textural Preferences

Among components of the consumer's perception of tomato fruit quality, texture is one of the most critical, being formed of a number of attributes that are difficult to evaluate and which change during fruit ripening and storage. The present work relates different textural parameters measured in a collection of traditional tomatoes from Madrid with the appreciation collected from consumer opinions. Texture traits were highly variable in studied tomatoes; they had strong environmental influence and they differed among genotypes. Great intrapopulational variability has been also detected, probably due to the heterogeneity of its fruit tissues and the intrapopulational diversity of studied landraces. Textural behavior of tomatoes affected the level of consumer appreciation. In general, consumers preferred firm fruits with soft skin, due to their low level of springiness and resilience. However, different taster groups have demonstrate how diversity appears to be necessary to satisfy different types of consumers. Among the three groups of consumers which have been identified in this study, the majority (83% of them) preferred local tomato varieties. In addition, heirloom tomatoes presented softer skins than the commercial hybrid reference (values of 0.29 ± 0.19 vs. 0.49 ± 0.12 kg regarding skin rupture force). That circumstance, which is involved in the post-harvest behavior, must be further considered in storage and commercialization.

Effect of Drying Methods on Volatile Compounds of Burdock (Arctium lappa L.) Root Tea as Revealed by Gas Chromatography Mass Spectrometry-Based Metabolomics

Burdock (Arctium lappa L.) is one of the nutritional foods widely planted in many countries. Dried burdock root (BR) is available as a herbal tincture and tea in many Asian countries with good flavor and taste. In this study, the volatile components in dried BR were identified and the effects of different drying methods on the volatile components were investigated by HS-GC-MS method. A total of 49 compounds were identified. Different drying methods including hot-air drying (HD, at 50, 60, 70, and 80 °C), vacuum drying (VD, at 50, 60, 70, and 80 °C), sunlight drying (SD), natural drying (ND), and vacuum freeze drying (VFD) were evaluated by HS-GC-MS-based metabolomics method. Results showed that different drying methods produced different effects on the volatile compounds. It was observed that 2,3-pentanedione, 1-(1H-pyrrol-2-yl)-ethanone, furfural, and heptanal were detected at higher concentrations in HD 80 and VD 70. The traditional HD and SD methods produced more flavor substances than VFD. The BR treated by the VFD method could maintain the shape of the fresh BR pieces while HD50 and VD80 methods could maintain the color of fresh BR pieces. These findings could help better understand the flavor of the corresponding processed BR and provide a guide for the drying and processing of BR tea.