Nondestructive measurement of fresh tomato lycopene content and other physicochemical characteristics using visible-NIR spectroscopy

Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition

Introduction

Precision monitoring maturity in climacteric fruits like tomato is crucial for minimising losses within the food supply chain and enhancing pre- and post-harvest production and utilisation.

Objectives

This paper introduces an approach to analyse the precision maturation of tomato using hyperspectral tomography-like.

Methods

A novel bi-directional spectral reconstruction method is presented, leveraging visible to near-infrared (Vis-NIR) information gathered from tomato spectra and their internal tissues (skin, pulp, and seeds). The study, encompassing 118 tomatoes at various maturation stages, employs a multi-block hierarchical principal component analysis combined with partial least squares for bi-directional reconstruction. The approach involves predicting internal tissue spectra by decomposing the overall tomato spectral information, creating a superset with eight latent variables for each tissue. The reverse process also utilises eight latent variables for reconstructing skin, pulp, and seed spectral data.

Results

The reconstruction of the tomato spectra presents a mean absolute percentage error of 30.44 % and 5.37 %, 5.25 % and 6.42 % and Pearson's correlation coefficient of 0.85, 0.98, 0.99 and 0.99 for the skin, pulp and seed, respectively. Quality parameters, including soluble solid content (%), chlorophyll (a.u.), lycopene (a.u.), and puncture force (N), were assessed and modelled with PLS with the original and reconstructed datasets, presenting a range of R2 higher than 0.84 in the reconstructed dataset. An empirical demonstration of the tomato maturation in the internal tissues revealed the dynamic of the chlorophyll and lycopene in the different tissues during the maturation process.

Conclusion

The proposed approach for inner tomato tissue spectral inference is highly reliable, provides early indications and is easy to operate. This study highlights the potential of Vis-NIR devices in precision fruit maturation assessment, surpassing conventional labour-intensive techniques in cost-effectiveness and efficiency. The implications of this advancement extend to various agronomic and food chain applications, promising substantial improvements in monitoring and enhancing fruit quality.

In Situ Visible Spectroscopic Daily Monitoring of Senescence of Japanese Maple (Acer palmatum) Leaves

The degradation of green leaves in autumn after their photosynthetic activities is associated with decreases in chlorophylls and increases in anthocyanins. However, the sequential orders of these processes are not well understood because of a lack of continuous monitoring of leaves in the same positions. Therefore, the senescence processes of Japanese maple (Acer palmatum) leaves were followed daily in the same positions for approximately 60 days using visible spectroscopy with an original handheld visible-near-infrared spectrometer. The obtained reflection spectra were converted to absorption spectra and band areas of chlorophyll a and anthocyanins were determined. Decreases in the chlorophyll a band area with time show two-step exponential decreases corresponding to slow and fast first-order decrease rates. A rapid decrease in chlorophyll a started after an increase in anthocyanin. Therefore, the leaf senescence started through a slow decrease in chlorophyll a (20-30 days), followed by a rapid increase in anthocyanins (~20 days), followed by a rapid decrease in chlorophyll a (10-20 days). The formation of anthocyanins has been proposed to protect leaf cells from losing chlorophylls through solar radiation damage. The obtained sequential changes of pigments support this light screen hypothesis. (199 words < 200 words).

Application of Near Infrared Spectroscopy for the Rapid Assessment of Nutritional Quality of Different Strawberry Cultivars

Strawberry is the most cultivated berry fruit globally and it is really appreciated by consumers because of its characteristics, mainly bioactive compounds with antioxidant properties. During the breeding process, it is important to assess the quality characteristics of the fruits for a better selection of the material, but the conventional approaches involve long and destructive lab techniques. Near infrared spectroscopy (NIR) could be considered a valid alternative for speeding up the breeding process and is not destructive. In this study, a total of 216 strawberry fruits belonging to four different cultivars have been collected and analyzed with conventional lab analysis and NIR spectroscopy. In detail, soluble solid content, acidity, vitamin C, anthocyanin, and phenolic acid have been determined. Partial least squares discriminant analysis (PLS-DA) models have been developed to classify strawberry fruits belonging to the four genotypes according to their quality and nutritional properties. NIR spectroscopy could be considered a valid non-destructive phenotyping method for monitoring the nutritional parameters of the fruit and ensuring the fruit quality, speeding up the breeding program.

Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content

Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.

Effect of Calcium Foliar Spray Technique on Mechanical Properties of Strawberries

The calcium fertilization of strawberry plants (Fragaria × ananassa Duchesne) was evaluated using two types of nozzles, with two liquid pressure levels and two driving speeds. The calcium content of the leaves and fruit were analyzed via flame photometry. Higher leaf calcium content was found in plots sprayed with standard nozzles, while higher fruit calcium content was observed for those sprayed with air induction nozzles. The fruit quality was assessed by determining the basic physical and mechanical properties, using uniaxial compression tests integrated with surface pressure measurements. Different spraying techniques influenced the mechanical resistance of the fruit. A spraying speed of 5 km/h and an operating pressure of 0.4 MPa significantly increased the firmness of the fruit by ~66%, the critical load level by 36%, and the maximum surface pressure by up to 38%, but did not increase the geometrical parameters of the strawberries. Regular foliar feeding during harvest could improve the mechanical strength of strawberries. An appropriate spraying technique with a calcium agent could effectively improve the mechanical properties of the delicate fruit, which is particularly important for limiting losses during harvesting, transportation, and storage.

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.

Vis-NIR Hyperspectral Imaging for Online Quality Evaluation during Food Processing: A Case Study of Hot Air Drying of Purple-Speckled Cocoyam (Colocasia esculenta (L.) Schott)

In this study, hyperspectral imaging (HSI) and chemometrics were implemented to develop prediction models for moisture, colour, chemical and structural attributes of purple-speckled cocoyam slices subjected to hot-air drying. Since HSI systems are costly and computationally demanding, the selection of a narrow band of wavelengths can enable the utilisation of simpler multispectral systems. In this study, 19 optimal wavelengths in the spectral range 400–1700 nm were selected using PLS-BETA and PLS-VIP feature selection methods. Prediction models for the studied quality attributes were developed from the 19 wavelengths. Excellent prediction performance (RMSEP < 2.0, r2P > 0.90, RPDP > 3.5) was obtained for MC, RR, VS and aw. Good prediction performance (RMSEP < 8.0, r2P = 0.70–0.90, RPDP > 2.0) was obtained for PC, BI, CIELAB b*, chroma, TFC, TAA and hue angle. Additionally, PPA and WI were also predicted successfully. An assessment of the agreement between predictions from the non-invasive hyperspectral imaging technique and experimental results from the routine laboratory methods established the potential of the HSI technique to replace or be used interchangeably with laboratory measurements. Additionally, a comparison of full-spectrum model results and the reduced models demonstrated the potential replacement of HSI with simpler imaging systems.

Portable near Infrared Spectroscopy as a Tool for Fresh Tomato Quality Control Analysis in the Field

Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes non-destructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way.

Nature versus Nurture: Assessing the Impact of Strain Diversity and Pregrowth Conditions on Salmonella enterica, Escherichia coli, and Listeria Species Growth and Survival on Selected Produce Items

Inoculation studies are important when assessing microbial survival and growth in food products. These studies typically involve the pregrowth of multiple strains of a target pathogen under a single condition; this emphasizes strain diversity. To gain a better understanding of the impacts of strain diversity ("nature") and pregrowth conditions ("nurture") on subsequent bacterial growth in foods, we assessed the growth and survival of Salmonella enterica (n = 5), Escherichia coli (n = 6), and Listeria (n = 5) inoculated onto tomatoes, precut lettuce, and cantaloupe rind, respectively. Pregrowth conditions included (i) 37°C to stationary phase (baseline), (ii) low pH, (iii) high salt, (iv) reduced water activity, (v) log phase, (vi) minimal medium, and (vii) 21°C. Inoculated tomatoes were incubated at 21°C; lettuce and cantaloupe were incubated at 7°C. Bacterial counts were assessed over three phases, including initial reduction (phase 1), change in bacterial numbers over the first 24 h of incubation (phase 2), and change over the 7-day incubation (phase 3). E. coli showed overall decline in counts (<1 log) over the 7-day period, except for a <1-log increase after pregrowth in high salt and to mid-log phase. In contrast, S. enterica and Listeria showed regrowth after an initial reduction. Pregrowth conditions had a substantial and significant effect on all three phases of S. enterica and E. coli population dynamics on inoculated produce, whereas strain did not show a significant effect. For Listeria, both pregrowth conditions and strain affected changes in phase 2 but not phases 1 and 3.IMPORTANCE Our findings suggest that inclusion of multiple pregrowth conditions in inoculation studies can best capture the range of growth and survival patterns expected for Salmonella enterica and Escherichia coli present on produce. This is particularly important for fresh and fresh-cut produce, where stress conditions encountered by pathogens prior to contamination can vary widely, making selection of a typical pregrowth condition virtually impossible. Pathogen growth and survival data generated using multiple pregrowth conditions will allow for more robust microbial risk assessments that account more accurately for uncertainty.

Assessment of Tomato Maturity in Different Layers by Spatially Resolved Spectroscopy

Tomato maturity is important to determine the fruit shelf life and eating quality. The objective of this research was to evaluate tomato maturity in different layers by using a newly developed spatially resolved spectroscopic system over the spectral region of 550-1650 nm. Thirty spatially resolved spectra were obtained for 600 tomatoes, 100 for each of the six maturity stages (i.e., green, breaker, turning, pink, light red, and red). Support vector machine discriminant analysis (SVMDA) models were first developed for each of individual spatially resolved (SR) spectra to compare the classification results of two sides. The mean spectra of two sides with the same source-detector distances were employed to determine the model performance of different layers. SR combination by averaging all the SR spectra was also subject to comparison with the classification model performance. The results showed large source-detector distances would be helpful for evaluating tomato maturity, and the mean_SR 15 obtained excellent classification results with the total classification accuracy of 98.3%. Moreover, the classification results were distinct for two sides of the probe, which demonstrated even if in the same source-detector distances, the classification results were influenced by the measurement location due to the heterogeneity for tomato. The mean of all SR spectra could only improve the classification results based on the first three mean_SR spectra, but could not obtain the accuracy as good as the following mean_SR spectra. This study demonstrated that spatially resolved spectroscopy has potential for assessing tomato maturity in different layers.

Prediction of α-Solanine and α-Chaconine in Potato Tubers from Hunter Color Values and VIS/NIR Spectra

The glycoalkaloids contents of potato tubers are usually measured by the destructive analysis that consumes time and requires expensive high-performance equipment. This study was carried out to determine the possibility of nondestructive estimation of α-solanine and α-chaconine content in potato tubers. Visible/near-infrared (VIS/NIR) spectra, color values, and the reference α-solanine and α-chaconine were measured from 180 tubers of ‘Atlantic’ and ‘Trent’ potato cultivars with eight replications at two-week intervals during the storage up to ten weeks. The partial least square (PLS) regression method was used to develop models correlating color and spectra data to the measured reference data. Regression coefficient (r) between color variables (Hunter $a^{\ast }$ , ${\text{a}}^{\ast }/{\text{b}}^{\ast }$ , and ( ${\text{a}}^{\ast }/{\text{b}}^{\ast }$ )2) and the actual measured values of a-solanine and a-chaconine content were 0.74, 0.62, and 0.62 and 0.70, 0.58, and 0.57, respectively, for the prediction set. Concurrently, equations were developed from color variables in multiple regression with r-values of 0.76 and 0.71 for α-solanine and α-chaconine, respectively. Additionally, the selected PLS model of VIS/NIR spectra had promising predictive power for α-solanine and α-chaconine with r-values of 0.68 and 0.63, respectively, between measured and predicted samples. Taken together, although it requires further studies to improve the prediction power of the developed models, the results of this study revealed the possibility of using VIS/NIR spectra and color variables for the prediction of α-solanine and α-chaconine contents from intact unpeeled potato tubers with chemical-free, fast, and cheap assessment methods.

Sonozonation: Enhancing the antimicrobial efficiency of aqueous ozone washing techniques on cherry tomato

Ultrasound requires high power and longer treatment times to inactivate microorganisms when compared to ultrasound combined with other technologies. Also, the antimicrobial efficiency of aqueous ozone increases with an increase in its concentration and exposure time, but with a detrimental effect on the quality of the treated food. In this study, the effect of aqueous ozone at low concentration, multi-mode frequency irradiation and their combination on microbial safety and nutritional quality of cherry tomato was investigated. Individual washing with aqueous ozone and mono-mode frequency irradiation resulted in <1 log CFU/g reduction in the spoilage microorganisms, while dual-mode frequency irradiation (DMFI) resulted in higher microbial reduction (1.3-2.6 1 log CFU/g). The combined system (20/40 kHz + aqueous ozone) on the other hand, resulted in >3 log CFU/g microbial reduction. The application of DMFI enhanced the antimicrobial efficiency of aqueous ozone without any detrimental effect on the physicochemical properties (except the firmness), bioactive compounds, and antioxidants of the cherry tomato during 21 days refrigerated storage. The result obtained indicates the promising substitute to the single washing technique for microbial safety as well as preserving the nutritional quality and enhancing the shelf life of cherry tomato.

Application of the Non-Destructive NIR Technique for the Evaluation of Strawberry Fruits Quality Parameters

The determination of strawberry fruit quality through the traditional destructive lab techniques has some limitations related to the amplitude of the samples, the timing and the applicability along all phases of the supply chain. The aim of this study was to determine the main qualitative characteristics through traditional lab destructive techniques and Near Infrared Spectroscopy (NIR) in fruits of five strawberry genotypes. Principal Component Analysis (PCA) was applied to search for spectral differences among all the collected samples. A Partial Least Squares regression (PLS) technique was computed in order to predict the quality parameters of interest. The PLS model for the soluble solids content prediction was the best performing-in fact, it is a robust and reliable model and the validation values suggested possibilities for its use in quality applications. A suitable PLS model is also obtained for the firmness prediction-the validation values tend to worsen slightly but can still be accepted in screening applications. NIR spectroscopy represents an important alternative to destructive techniques, using the infrared region of the electromagnetic spectrum to investigate in a non-destructive way the chemical-physical properties of the samples, finding remarkable applications in the agro-food market.

In situ ripening stages monitoring of Lamuyo pepper using a new-generation near-infrared spectroscopy sensor

BACKGROUND

Near-infrared spectroscopy (NIRS) was used as a nondestructive sensor to assess the quality of freshly harvested Lamuyo peppers. One hundred and forty-four Lamuyo peppers, which were in a range of colors (green, chocolate, orange, and red) when harvested, were analyzed. In this study, the evolution of the main quality parameters during the harvest period was analyzed. Additionally, NIRS predictive models using a portable manual spectrophotometer to evaluate quality parameters together with color index were developed. Moreover, two procedures for taking near-infrared spectra were tested: (1) static, where point spectral readings were taken of around the equator of the fruit; (2) dynamic, where spectra were taken by scanning the entire length of the pepper.

RESULTS

Green peppers and those harvested at the beginning of the campaign presented significantly lower values (P < 0.05) of dry matter, soluble solid contents, and titratable acidity, whereas those with red coloration and those harvested at the end of the campaign showed significantly higher values of these three quality parameters (P < 0.05). The predictive capacity of the NIRS models showed that the static mode proved to be the most suitable for measuring the quality of Lamuyo peppers.

CONCLUSIONS

The viability of NIRS for measuring dry matter content and soluble solid contents in situ, using a new-generation NIRS sensor, was demonstrated. However, the high water content, the irregular shape of the fruit, and the fact that it is hollow inside all point to the need for using larger samples sets so as to increase the robustness of the models obtained. © 2019 Society of Chemical Industry.

Effect of the Architecture of Fiber-Optic Probes Designed for Soluble Solid Content Prediction in Intact Sugar Beet Slices

Sugar beet is the second biggest world contributor to sugar production and the only one grown in Europe. One of the main limitations for its competitiveness is the lack of effective tools for assessing sugar content in unprocessed sugar beet roots, especially in breeding programs. In this context, a dedicated near infrared (NIR) fiber-optic probe based approach is proposed. NIR technology is widely used for the estimation of sugar content in vegetable products, while optic fibers allow a wide choice of technical properties and configurations. The objective of this research was to study the best architecture through different technical choices for the estimation of sugar content in intact sugar beet roots. NIR spectral measurements were taken on unprocessed sugar beet samples using two types of geometries, single and multiple fiber-probes. Sugar content estimates were more accurate when using multiple fiber-probes (up to R² = 0.93) due to a lesser disruption of light specular reflection. In turn, on this configuration, the best estimations were observed for the smallest distances between emitting and collecting fibers, reducing the proportion of multiply scattered light in the spectra. Error of prediction (RPD) values of 3.95, 3.27 and 3.09 were obtained for distances between emitting and collecting fibers of 0.6, 1.2 and 1.8 µm respectively. These high RPD values highlight the good predictions capacities of the multi-fiber probes. Finally, this study contributes to a better understanding of the effects of the technical properties of optical fiber-probes on the quality of spectral models. In addition, and beyond this specificity related to sugar beet, these findings could be extended to other turbid media for quantitative optical spectroscopy and eventually to validate considered fiber-optic probe design obtained in this experimental study.

Comparison of lycopene changes between open-field processing and fresh market tomatoes during ripening and post-harvest storage by using a non-destructive reflectance sensor

BACKGROUND

Accumulation and stability of tomato lycopene markedly depends on the cultivar, plant growing and storage conditions. To estimate lycopene in open-field cultivated processing and fresh market tomatoes, we used a calibrated spectral reflectance portable sensor.

RESULTS

Lycopene accumulation in fruits attached to the plant, starting from the Green ripening stage, followed a sigmoidal function. It was faster and reached higher levels in processing (cv. Calista) than fresh market (cv. Volna) tomatoes (90 and 62 mg kg^-1 fresh weight, respectively). During storage at 12, 20 and 25 °C, Red tomatoes retained about 90% of harvest lycopene for three weeks. Pink tomatoes increased lycopene during the first week of storage, but never reached the lycopene values of Red tomatoes ripened on the vine. Storability at 12 °C retaining the highest quality in red tomatoes was limited to 14 and 7 days for Calista and Volna cultivars, respectively.

CONCLUSION

Significant differences in lycopene accumulation and stability between processing and fresh market tomatoes were established by examining with time the very same fruits by a non-destructive optical tool. It can be useful in agronomical and post-harvest physiological studies and can be of interest for producers oriented to the niche nutraceutical market. © 2018 Society of Chemical Industry.

Model development for non-destructive determination of rind biochemical properties of 'Marsh' grapefruit using visible to near-infrared spectroscopy and chemometrics

Rind biochemical properties play major roles in defence mechanisms against the incidence of rind physiological disorders of citrus fruit during cold storage. Hence, multivariate calibration models were developed to rapidly and non-destructively determine rind biochemical properties of citrus fruit from visible to near-infrared (Vis/NIR) spectra acquired by Vis/NIR spectroscopy using partial least square regression algorithm. To achieve optimum models for determination of each rind biochemical property, several mathematical pre-processing methods were explored, including no pre-treatment. However, special emphases were given to the best model statistics in terms of coefficient of determination (R²) and residual predictive deviation (RPD). Models were performed by critical examination of different wavelength ranges (visible, near-infrared and full regions) and combinations of fruit harvested from different production regions and acquired before (week 0) and after (week 9) cold storage. Results obtained showed excellent models for determining parameters such as sucrose (R² = 0.99 and RPD = 11.42), total flavonoids (R² = 0.99 and RPD = 12.37), and chlorophyll b (R² = 0.97 and RPD = 5.67). This study reported the first application of Vis/NIR and chemometrics in determining the rind biochemical properties of 'Marsh' grapefruit rapidly and non-destructively.

Prediction Models for Assessing Lycopene in Open-Field Cultivated Tomatoes by Means of a Portable Reflectance Sensor: Cultivar and Growing-Season Effects

Reflectance spectroscopy represents a useful tool for the nondestructive assessment of tomato lycopene, even in the field. For this reason, a compact, low-cost, light emitting diode-based sensor has been developed to measure reflectance in the 400-750 nm spectral range. It was calibrated against wet chemistry and evaluated by partial least squares (PLS) regression analyses. The lycopene prediction models were defined for two open-field cultivated red-tomato varieties: the processing oblong tomatoes of the cv. Calista (average weight: 76 g) and the fresh-consumption round tomatoes of the cv. Volna (average weight: 130 g), over a period of two consecutive years. The lycopene prediction models were dependent on both cultivar and season. The lycopene root mean square error of prediction produced by the 2014 single-cultivar calibrations validated on the 2015 samples was large (33 mg kg^-1) in the Calista tomatoes and acceptable (9.5 mg kg^-1) in the Volna tomatoes. A more general bicultivar and biyear model could still explain almost 80% of the predicted lycopene variance, with a relative error in red tomatoes of less than 20%. In 2016, the in-field applications of the multiseasonal prediction models, built with the 2014 and 2015 data, showed significant ( P < 0.001) differences in the average lycopene estimated in the crop on two sampling dates that were 20 days apart: on August 19 and September 7, 2016, the lycopene was 98.9 ± 9.3 and 92.2 ± 10.8 mg kg^-1 FW for cv. Calista and 54.6 ± 13.2 and 60.8 ± 6.8 mg kg^-1 FW for cv. Volna. The sensor was also able to monitor the temporal evolution of lycopene accumulation on the very same fruits attached to the plants. These results indicated that a simple, compact reflectance device and PLS analysis could provide adequately precise and robust (through-seasons) models for the nondestructive assessment of lycopene in whole tomatoes. This technique could guarantee tomatoes with the highest nutraceutical value from the production, during storage and distribution, and finally to consumers.

Postharvest Monitoring of Tomato Ripening Using the Dynamic Laser Speckle

The dynamic laser speckle (biospeckle) method was tested as a potential tool for the assessment and monitoring of the maturity stage of tomatoes. Two tomato cultivars—Admiro and Starbuck—were tested. The process of climacteric maturation of tomatoes was monitored during a shelf life storage experiment. The biospeckle phenomena were captured using 640 nm and 830 nm laser light wavelength, and analysed using two activity descriptors based on biospeckle pattern decorrelation—C4 and ε. The well-established optical parameters of tomatoes skin were used as a reference method (luminosity, a*/b*, chroma). Both methods were tested with respect to their prediction capabilities of the maturity and destructive indicators of tomatoes—firmness, chlorophyll and carotenoids content. The statistical significance of the tested relationships were investigated by means of linear regression models. The climacteric maturation of tomato fruit was associated with an increase in biospckle activity. Compared to the 830 nm laser wavelength the biospeckle activity measured at 640 nm enabled more accurate predictions of firmness, chlorophyll and carotenoids content. At 640 nm laser wavelength both activity descriptors (C4 and ε) provided similar results, while at 830 nm the ε showed slightly better performance. The linear regression models showed that biospeckle activity descriptors had a higher correlation with chlorophyll and carotenoids content than the a*/b* ratio and luminosity. The results for chroma were comparable with the results for both biospeckle activity indicators. The biospeckle method showed very good results in terms of maturation monitoring and the prediction of the maturity indices of tomatoes, proving the possibility of practical implementation of this method for the determination of the maturity stage of tomatoes.

Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction-A Review

Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre- and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in-field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshouse) assessment. This review focuses on the non-destructive methods which are promising for, or have already been applied to, the pre-harvest in-field measurements including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed.

Robustness of Tomato Quality Evaluation Using a Portable Vis-SWNIRS for Dry Matter and Colour

The utility of a handheld visible-short wave near infrared spectrophotometer utilising an interactance optical geometry was assessed in context of the noninvasive determination of intact tomato dry matter content, as an index of final ripe soluble solids content, and colouration, as an index of maturation to guide a decision to harvest. Partial least squares regression model robustness was demonstrated through the use of populations of different harvest dates or growing conditions for calibration and prediction. Dry matter predictions of independent populations of fruit achieved R² ranging from 0.86 to 0.92 and bias from -0.14 to 0.03%. For a CIE a^⁎ colour model, prediction R² ranged from 0.85 to 0.96 and bias from -1.18 to -0.08. Updating the calibration model with new samples to extend range in the attribute of interest and in sample matrix is key to better prediction performance. The handheld spectrometry system is recommended for practical implementation in tomato cultivation.

Modeling Tomato Ripening Based on Carotenoid Raman Spectroscopy: Experimental Versus Kinetic Model

This work reports on a combined experimental and theoretical investigation on the carotenoid Raman signal in several tomato fruits during their postharvest time evolution and ripening. Both resonant (180°) backscattering and polarized (90°) Raman scattering were used to monitor the most prominent bands of carotenoid (lycopene and β-carotene) evolution in different tomato varieties. Relevant findings of the present investigations were that while the depolarization ratio of the ν₁ band hardly changed with time, the Raman ν₁ band intensity did change showing a similar pattern for all tomatoes investigated. Indeed, all cases investigated revealed a rise of the carotenoid signal coincident with the onset of the turning stage of the fruit ripening, a pronounced maximum of the Raman signal followed by a post-maximum decline at the red ripening stage. A kinetic model has been developed to describe the time evolution of the observed Raman signatures based on the rate coefficient of the carotenoid synthesis and the time evolution of the scattering coefficient of the fruit. The model describes satisfactorily the tomato evolution through the distinct ripening stages providing new insight on the assessment of the postharvest fruit control and quality.

Structures and Analysis of Carotenoid Molecules

Modifications of the usual C40 linear and symmetrical carotenoid skeleton give rise to a wide array of structures of carotenes and xanthophylls in plant tissues. These include acyclic, monocyclic and dicyclic carotenoids, along with hydroxy and epoxy xanthophylls and apocarotenoids. Carotenols can be unesterified or esterified (monoester) in one or two (diester) hydroxyl groups with fatty acids. E-Z isomerization increases the array of possible plant carotenoids even further. Screening and especially quantitative analysis are being carried out worldwide. Visible absorption spectrometry and near infrared reflectance spectroscopy have been used for the initial estimation of the total carotenoid content or the principal carotenoid content when large numbers of samples needed to be analyzed within a short time, as would be the case in breeding programs. Although inherently difficult, quantitative analysis of the individual carotenoids is essential. Knowledge of the sources of errors and means to avoid them has led to a large body of reliable quantitative compositional data on carotenoids. Reverse-phase HPLC with a photodiode array detector has been the preferred analytical technique, but UHPLC is increasingly employed. HPLC-MS has been used mainly for identification and NMR has been useful in unequivocally identifying geometric isomers.

Testing of a simplified LED based vis/NIR system for rapid ripeness evaluation of white grape (Vitis vinifera L.) for Franciacorta wine

The aim of this work was to test a simplified optical prototype for a rapid estimation of the ripening parameters of white grape for Franciacorta wine directly in field. Spectral acquisition based on reflectance at four wavelengths (630, 690, 750 and 850 nm) was proposed. The integration of a simple processing algorithm in the microcontroller software would allow to visualize real time values of spectral reflectance. Non-destructive analyses were carried out on 95 grape bunches for a total of 475 berries. Samplings were performed weekly during the last ripening stages. Optical measurements were carried out both using the simplified system and a portable commercial vis/NIR spectrophotometer, as reference instrument for performance comparison. Chemometric analyses were performed in order to extract the maximum useful information from optical data. Principal component analysis (PCA) was performed for a preliminary evaluation of the data. Correlations between the optical data matrix and ripening parameters (total soluble solids content, SSC; titratable acidity, TA) were carried out using partial least square (PLS) regression for spectra and using multiple linear regression (MLR) for data from the simplified device. Classification analysis were also performed with the aim of discriminate ripe and unripe samples. PCA, MLR and classification analyses show the effectiveness of the simplified system in separating samples among different sampling dates and in discriminating ripe from unripe samples. Finally, simple equations for SSC and TA prediction were calculated.

Rapid phenotyping of the tomato fruit model, Micro-Tom, with a portable VIS-NIR spectrometer

Tomato (Solanum lycopersicum) quality traits such as juice soluble solid content (Brix), juice pH, color parameters (Hue and Chroma), firmness and water content, are critical factors for fruit quality assessment. The need for screening very large numbers of fruit has led to the development of a high-throughput method using visible-near infrared (VIS-NIR) spectrometry. We are reporting here a set of results obtained with a portable spectrometer using the 350-2500 nm range, showing good prediction of the quality traits cited above, over a wide range of developmental stages from immature green to ripe tomato fruit, cv. Micro-Tom. This is a rather good set of quality traits compared to previous publications predicting tomato quality with VIS-NIR spectrometry, and the prediction is robust, as it was obtained by grouping sets of different operators. This would be a useful tool to phenotype hundreds of Micro-Tom per day, making it possible to follow the dynamics of the described parameters on growing fruits. Thus the method can be used to study the biochemistry and physiology of fruit development in planta.