Quantification of Lycopene, β-Carotene, and Total Soluble Solids in Intact Red-Flesh Watermelon (Citrullus lanatus) Using On-Line Near-Infrared Spectroscopy

A Review of the Chemistry and Biological Activities of Natural Colorants, Dyes, and Pigments: Challenges, and Opportunities for Food, Cosmetics, and Pharmaceutical Application

Natural pigments are important sources for the screening of bioactive lead compounds. This article reviewed the chemistry and therapeutic potentials of over 570 colored molecules from plants, fungi, bacteria, insects, algae, and marine sources. Moreover, related biological activities, advanced extraction, and identification approaches were reviewed. A variety of biological activities, including cytotoxicity against cancer cells, antioxidant, anti-inflammatory, wound healing, anti-microbial, antiviral, and anti-protozoal activities, have been reported for different pigments. Considering their structural backbone, they were classified as naphthoquinones, carotenoids, flavonoids, xanthones, anthocyanins, benzotropolones, alkaloids, terpenoids, isoprenoids, and non-isoprenoids. Alkaloid pigments were mostly isolated from bacteria and marine sources, while flavonoids were mostly found in plants and mushrooms. Colored quinones and xanthones were mostly extracted from plants and fungi, while colored polyketides and terpenoids are often found in marine sources and fungi. Carotenoids are mostly distributed among bacteria, followed by fungi and plants. The pigments isolated from insects have different structures, but among them, carotenoids and quinone/xanthone are the most important. Considering good manufacturing practices, the current permitted natural colorants are: Carotenoids (canthaxanthin, β-carotene, β-apo-8'-carotenal, annatto, astaxanthin) and their sources, lycopene, anthocyanins, betanin, chlorophyllins, spirulina extract, carmine and cochineal extract, henna, riboflavin, pyrogallol, logwood extract, guaiazulene, turmeric, and soy leghemoglobin.

Prediction and classification of soluble solid contents to determine the maturity level of watermelon using visible and shortwave near infrared spectroscopy

The present work investigated the potential application of a portable and low-cost spectroscopic technique to predict the soluble solid content (SSC) for determining the maturity level of watermelons. A total of 63 watermelon samples were used in the present work, representing three different maturity levels: unmatured, matured, and over-matured. Before spectral acquisition, each watermelon sample was cut into half, producing 126 fruit portions. Visible shortwave near infrared (VSNIR) spectrometer was used to record the spectral data from the skin surface of each portion. The SSC of each portion was measured using a digital refractometer. Partial least square (PLS) regression method was used to establish both calibration and prediction models to predict the SSC values from the watermelon samples. Support vector machine (SVM) classifier was used to categorise spectral data into the respective maturity levels. Results showed that the coefficient of determination (R2) values for calibration models of unmatured, matured, and over-matured were 0.65, 0.81, and 0.78, respectively. For the prediction model, the R2 values for unmatured, matured, and over-matured were 0.60, 0.74, and 0.76, respectively. The SVM yielded good classification accuracy of 85%. The present work demonstrated that the proposed spectroscopic method could be applied to predict and classify the maturity level of watermelons based on their skin condition.

Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling

The primary issues in collecting biochemical information in a large area using chemical laboratory procedures are low throughput, hard work, time-consuming, and requiring several samples. Thus, real-time and precise estimation of biochemical variables of various fruits using a proximal remote sensing based on spectral reflectance is critical for harvest time, artificial ripening, and food processing, which might be beneficial economically and ecologically. The main goal of this study was to assess the biochemical parameters of banana fruits such as chlorophyll a (Chl a), chlorophyll b (Chl b), respiration rate, total soluble solids (TSS), and firmness using published and newly developed spectral reflectance indices (SRIs), integrated with machine learning modeling (Artificial Neural Networks; ANN and support vector machine regression; SVMR) at different ripening degrees. The results demonstrated that there were evident and significant differences in values of SRIs at different ripening degrees, which may be attributed to the large variations in values of biochemical parameters. The newly developed two-band SRIs are more effective at measuring different biochemical parameters. The SRIs that were extracted from the visible (VIS), near-infrared (NIR), and their combination showed better R2 with biochemical parameters. SRIs combined with ANN and SVMR would be an effective method for estimating five biochemical parameters in the calibration (Cal.) and validation (Val.) datasets with acceptable accuracy. The ANN-TSS-SRI-13 model was built to determine TSS with greater performance expectations (R2 = 1.00 and 0.97 for Cal. and Val., respectively). Furthermore, the model ANN-Firmness-SRI-15 was developed for determining firmness, and it performed better (R2 = 1.00 and 0.98 for Cal. and Val., respectively). In conclusion, this study revealed that SRIs and a combination approach of ANN and SVMR models would be a useful and excellent tool for estimating the biochemical characteristics of banana fruits.

Insights on the role of chemometrics and vibrational spectroscopy in fruit metabolite analysis

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The use of vibrational spectroscopy combined with data analytics is discussed.

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The measure of bioactive compounds metabolites in fruit samples is presented.

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Advantages and limitations of these techniques are discussed.

The last three decades have demonstrated the ability of combining data analytics (e.g. big data, machine learning) with modern analytical instrumental techniques such as vibrational spectroscopy (VIBSPEC) (e.g. NIR, Raman, MIR) and sensing technologies (e.g. electronic noses and tongues, colorimetric sensors) to analyse, measure and monitor a wide range of properties and samples. Developments in instrumentation, hardware and software have placed VIBSPEC as a useful tool to quantify several bioactive compounds and metabolites in a wide range of fruit and plant samples. With the incorporation of hand-held and portable instrumentation, these techniques have been valuable for the development of in-field and high throughput applications, opened new frontiers of analysis in fruits and plants. This review will present and discuss some of the current applications on the use of VIBSPEC techniques combined with data analytics on the measurement bioactive compounds and plant metabolites in different fruit samples.

Current Status of Optical Systems for Measuring Lycopene Content in Fruits: Review

Optical systems are used for analysing the internal composition and the external properties in food. The measurement of the lycopene content in fruits and vegetables is important because of its benefits to human health. Lycopene prevents cardiovascular diseases, cataracts, cancer, osteoporosis, male infertility, and peritonitis. Among the optical systems focused on the estimation and identification of lycopene molecule are high-performance liquid chromatography (HPLC), the colorimeter, infrared near NIR spectroscopy, UV-VIS spectroscopy, Raman spectroscopy, and the systems of multispectral imaging (MSI) and hyperspectral imaging (HSI). The main objective of this paper is to present a review of the current state of optical systems used to measure lycopene in fruits. It also reports important factors to be considered in order to improve the design and implementation of those optical systems. Finally, it was observed that measurements with HPLC and spectrophotometry present the best results but use toxic solvents and require specialized personnel for their use. Moreover, another widely used technique is colorimetry, which correlates the lycopene content using color descriptors, typically those of CIELAB. Likewise, it was identified that spectroscopic techniques and multispectral images are gaining importance because they are fast and non-invasive.

Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool

Piperine is a bioactive alkaloid compound which provides a unique spicy flavor derived from plants of the Piper nigrum L. Black pepper (n = 160) collected from Vietnam was studied using non-destructive near infrared spectroscopy (NIRS). The spectral acquisition ranged from 1100 to 2500 nm, and a chemometrics analysis program was performed to quantify the piperine contents. High performance liquid chromatography (HPLC) analysis was carried out to develop a chemometric model based on reference values. The black pepper samples were divided into two groups used for calibration (n = 120) and prediction (n = 40) sets. The optimum calibration model was developed by pretreatment of the spectra. The analyses results based on the prediction samples included a coefficient of determination (R2) of 0.914, a root mean square error of prediction (RMSEP) and a standard error of prediction (SEP) of about 0.220 g/100 g, and a ratio performance to deviation (RPD) value of 3.378 regarding the partial least square (PLS) regression model, and an R2 of 0.921, an RMSEP and SEP of 0.210 g/100 g, and an RPD of 3.571, with respect to the principal components (PC) regression model. These results indicate that NIRS can be applicable as a control, or as an alternative rapid and effective method to quantify piperine in P. nigrum L.

An Overview of Properties and Analytical Methods for Lycopene in Organic Nanocarriers

Lycopene (LYC), a natural compound responsible for the red color of some fruits like pink grapefruit, red guava, watermelon, papaya and, mainly, present in tomatoes (Solanum lycopersicum). LYC has been extensively studied because of its various pharmacological properties such as antioxidant, cardioprotective, hypocholesterolemic, antineophasic, photoprotection, antidiabetic and antimicrobial activity. However, LYC uses in therapy is limited due to its insolubility in aqueous solvents, resulting in low bioavailability and stability. In order to overcome these drawbacks, it is essential to use of organic nanocarriers for LYC controlled release. Up to now, the description of LYC-loaded organic nanocarriers are scarce, mainly related to organic nanosystems based on lipid nanostructures such as nanoemulsions (NE), liposomes (LP), niosomes (NI), nanostructured lipid carriers (NLC) and solid lipid nanoparticles (SLN). Taking into account the development of new formulations, is indispensable the use of sensitive and suitable analytical methods previously validated. Among the analytical methods described here, high-performance liquid chromatography (HPLC) stands out due to its good accuracy, precision and desirable detection limit. In this review, we highlights the main biological and physicochemical properties of LYC, as well as LYC-based organic nanocarriers for controlled drug delivery and the analytical methods described in literature to determine LYC in any kind of matrix.

Evolution of volatile compounds in 'Cuoredolce®' and 'Rugby' mini- watermelons (Citrullus lanatus (Thunb.) Matsumura and Nakai) in relation to ripening at harvest

BACKGROUND

Watermelon is appreciated for its nutritional properties and for its flavor. Among the flavor-active compounds that it contains, volatiles play a key role being responsible for aroma. Recent breeding activity has led to the release of mini-watermelons with reduced fruit weight. This paper reports on the characterization of aroma profiles of 'Rugby' and 'Cuoredolce®' novel mini-watermelon cultivars at the ripening stage. The main volatiles were identified and quantified using headspace solid-phase microextraction gas-chromatography mass spectrometry (HS-SPME-GC-MS), and their concentrations were correlated with the E-nose profile. The potential contribution of volatile compounds to the fruit aroma was evaluated by computing the odor activity values (OAV).

RESULTS

Twenty main volatile compounds were identified: aldehydes (9), alcohols (4), ketones (2), and terpenes and terpenoids (5). C-9 aldehydes and alcohols were the prevalent compounds. The two cultivars differed in precocity, with 'Rugby' being riper from the early stage considered. Many apocarotenoids with desirable olfactory notes were detected in the volatile profile of 'Rugby'. Four e-nose sensors' signals significantly changed with variety and ripening stage: W1W and W2W were positively correlated and W6S was negatively correlated with all identified volatiles, while W3S showed a negative correlation with 6-methyl-5-hepten-2-one, the major lycopene catabolite.

CONCLUSIONS

The aroma profiles described here contribute to the characterization of 'Cuoredolce®' and 'Rugby' mini-watermelon cultivars. Electronic-nose measurement was able to discriminate between cultivars and, to a lesser extent, among ripening stages. © 2019 Society of Chemical Industry.

Ability of near-infrared spectroscopy for non-destructive detection of internal insect infestation in fruits: Meta-analysis of spectral ranges and optical measurement modes

The feasibility of utilizing near-infrared (NIR) spectroscopy has been recently assessed for rapid and non-destructive detection of internal insect infestation in some fruits. Based on the findings, this technology can be used for on/in-line inspection of the fruits in terms of insect infestation if suitable instrument is selected for accurate spectral measurements and system development. The spectral range and optical measurement mode are two of the most important factors which can affect the accuracy of the spectral measurements and detection models. The aim of this study is meta-analysis of these factors' effects on the ability of NIR-based spectroscopy for non-destructive detection of hidden insect infestation in fruits. Eight studies (65 observations) were extracted based on the criteria of this study. Overall, utilizing NIR-based spectroscopy led to 13.98% error (95% CI = 10.69-17.27%) for non-destructive detection of hidden insect infestation in fruits. Spectral ranges of Vis/SWNIR (above 300 up to 1100 nm), NIR (above 780 up to 2500 nm), and Vis/NIR (above 300 up to 2500 nm) showed errors of 21.71% (95% CI = 16.56-26.86%), 13.30% (95% CI = 5.24-21.36%), and 13.65% (95% CI = 5.9-21.4%), respectively. It was noted that wavelengths above 1100 nm (NIR region) are more useful to detect insect infestation in fruit. Results also indicated that optical measurement modes of interactance, reflectance, and transmittance had errors of 6.66% (95% CI = 4.18-9.14%), 15.73% (95% CI = 10.99-20.47%), and 16.04% (95% CI = 7.26-24.82%), respectively. This meta-analysis suggests that utilizing interactance mode for spectra measurement in NIR-based spectroscopy can increase the accuracy of discrimination of insect infested fruits especially when the spectral range of the spectrometer is Vis/SWNIR. Moreover, it should be selected a spectrometer with the wavelength range of NIR or Vis/NIR when using reflectance or transmittance mode is necessary for developing an in/on-line system to detect insect infestation in fruits.

Pattern recognition-based optical technique for non-destructive detection of Ectomyelois ceratoniae infestation in pomegranates during hidden activity of the larvae

In this research, the feasibility of utilizing visible/near-infrared (Vis/NIR) spectroscopy as an optical non-destructive technique combined with both supervised and unsupervised pattern recognition methods was assessed for detection of Ectomyelois ceratoniae, carob moth, infestation in pomegranates during hidden activity of the larvae. To this end, some fruits were artificially contaminated to the carob moth larvae. Vis/NIR spectra of the blank samples and the contaminated pomegranates without and with external visual symptoms of larvae infestation were analyzed one and two weeks after contaminating the samples as three groups of "Healthy", "Unhealthy-A" and "Unhealthy-B", respectively. Principal component analysis (PCA) as unsupervised pattern recognition method was used to verify the possibility of clustering of the pomegranate samples into the three groups. Discriminant analysis (DA) based on PCA was also used as a powerful supervised pattern recognition method to classify the samples. The calibration models of linear, quadratic and Mahalanobis discriminant analyses were developed based on different spectral pre-processing techniques. The best PCA-DA model was obtained using Mahalanobis distance method and first derivative (D1) pre-processing. The total percentage of correctly classified samples with the best calibration model was 97.9%. The developed model could also predict unknown samples with total percentage of correctly classified samples of 90.6%. It was concluded that Vis/NIR spectroscopy combined with pattern recognition method of PCA-DA can be an appropriate and rapid technology for non-destructively screening the pomegranates for detection of carob moth infestation during hidden activity of the larvae.

Quantification of Total Phenolic and Carotenoid Content in Blackberries (Rubus Fructicosus L.) Using Near Infrared Spectroscopy (NIRS) and Multivariate Analysis

A rapid method to quantify the total phenolic content (TPC) and total carotenoid content (TCC) in blackberries using near infrared spectroscopy (NIRS) was carried out aiming to provide reductions in analysis time and cost for the food industry. A total of 106 samples were analysed using the Folin-Ciocalteu method for TPC and a method based on Ultraviolet-Visible Spectrometer for TCC. The average contents found for TPC and TCC were 24.27 mg·g⁻¹ dw and 8.30 µg·g⁻¹ dw, respectively. Modified partial least squares (MPLS) regression was used for obtaining the calibration models of these compounds. The RPD (ratio of the standard deviation of the reference data to the standard error of prediction (SEP)) values from external validation for both TPC and TCC were between 1.5 < RPDp < 2.5 and RER values (ratio of the range in the reference data to SEP) were 5.92 for TPC and 8.63 for TCC. These values showed that both equations were suitable for screening purposes. MPLS loading plots showed a high contribution of sugars, chlorophyll, lipids and cellulose in the modelling of prediction equations.

Daily Dietary Antioxidant Interactions Are Due to Not Only the Quantity but Also the Ratios of Hydrophilic and Lipophilic Phytochemicals

The hydrophilic extracts of mulberry (HEM) and blueberry (HEB) and lipophilic extracts of mango (LEM) and watermelon (LEW) were mixed in different ratios to assess the antioxidant interactions by chemical-based (DPPH and ABTS assays) and H9c2 cell-based models. There were both synergistic and antagonistic antioxidant interactions among these fruits. Some groups with combinational extracts showed stronger synergistic antioxidant effects than the individual groups, and others (HEM-LEW F1/10, LEW-LEM F5/10, and HEB-LEM F3/10) showed stronger antagonistic effects than the individual groups based on the indicators [the values of DPPH, ABTS, and MTT; the expression of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase (CAT), and malondialdehyde (MDA); the release of lactate dehydrogenase (LDH); and the quantification of cellular antioxidant activity]. The principal component analysis (PCA) showed that samples could be defined by two principal components: PC1, the main phenolic acids and anthocyanins, and PC2, carotenoids. From our results, primarily, carotenoids were in the majority in antagonistic groups, and phenolics and anthocyanins were in the majority in synergistic groups. However, the combinational groups containing only hydrophilic compounds did not always show synergistic effects. Therefore, the compatibility of diets indicates balancing the ratios of hydrophilic and lipophlic compounds in our daily food. In addition, the expression of enzymes (SOD, GSH-Px, and CAT) may not be sensitive to the changes of antioxidant activity caused by the combinations with different ratios of hydrophilic and lipophilc compounds. The different structures of lipophilic compounds (β-carotene and lycopene) could influence the antagonistic effects.

Structural Organization of 6B9 Molecule, a Monoclonal Antibody Against Lycopene

Full cDNA and corresponding amino acid (AA) sequences of 6B9 monoclonal antibody (mAb) against lycopene was obtained using Step-Out RACE technology. Variable (V) and constant (C) regions were identified. The light chain of 6B9 contained 238 AA IgM with the highest level of identity (0.93) to both the anti-VEGF receptor antibody and anti-collagen type II FAb CIIC1. The heavy chain was composed of 634 AA with a high identity (0.9) to the Ig mu chain C region. Potential posttranslational modification regions in both chains were identified alongside with disulfide bond sites. The obtained information can be used for making chimeric constructs containing 6B9 mAb (or its fragments) and lycopene, a powerful carotenoid with antioxidant as well as antiproliferating properties, which can be implemented in the treatment of an aggressive form of prostate cancer and possibly other malignancies.