Recent Advances in Nondestructive Analytical Techniques for Determining the Total Soluble Solids in Fruits: A Review

Development of a plant-based dessert using araticum pulp and chickpea extract: Physicochemical, microbiological, antioxidant, and sensory characterization

The demand for plant-based products has increased in recent years, due to several aspects related to health and environmental consciousness. This study aimed to produce and characterize a plant-based dairy alternative dessert based on araticum pulp and chickpea extract without added sugar and fat. Three formulations were prepared: Formulation 1 (F1): 20% araticum pulp + 80% chickpea extract; Formulation 2 (F2): 30% araticum pulp + 70% chickpea extract; and Formulation 3 (F3): 40% araticum pulp + 60% chickpea extract. All formulations' chemical composition, sensorial characteristics, viscosity, total phenolic content, antioxidant activity, and microbiological stability were analyzed during 28 days of storage at 4°C and a relative humidity of 23%. Energetic value ranged from 64 to 71 kcal/100g, and carbohydrate content from 9.68 to 11.06, protein from 3.38 to 3.04, lipids from 1.41 to 1.60, ashes from 0.53 to 0.59 and crude fiber from 0.86 to 1.34 g/100g among the formulations. The increase in the proportion of araticum pulp in the formulations reduced moisture content by 1.2 to 2.1% (F1: 84.2, F2: 83.2, and F3: 82.4), protein content by 3 to 9% (F1: 3.3, F2: 3.2, and F3: 3.0), and pH value by 5.8 to 10.7% (F1: 5.50, F2: 5.18, and F3: 4.91), and increased the TSS by 1.1 to 1.3-fold (F1: 8.36, F2: 8.98, and F3: 10.63 º Brix), total phenolics content by 1.5 to 2.0-fold (F1: 4,677, F2: 6,943, and F3: 10,112 gallic acid μmol/L) and antioxidant activity by 1.8 to 2.8-fold (F1: 1,974, F2: 3,664, and F3: 5.523). During the 28 days of storage at 4°C, the formulations F1 and F2 showed better stability of phenolic compounds and antioxidant activity; however, the formulation F3 showed acceptable microbiological quality up to 28 days of storage, higher viscosity, 8 to 16-fold higher than the formulations F1 and F2, respectively (F1: 238.90, F2: 474.30, and F3:3,959.77 mPa.s), antioxidant capacity and better scores in sensory analysis. The present study showed that the plant-based dessert elaborated with araticum pulp and chickpea extract might be considered a potential dairy alternative product with high antioxidant activity, protein content, and a viscosity similar to yogurt; however, its sensory aspects need improvement.

Spherical Fe2O3 nanoparticles inhibit the production of aflatoxins (B1 and B2) and regulate total soluble solids and titratable acidity of peach fruit

Aflatoxin is a group I carcinogen and causes significant public health and food safety risks, throughout the world. This study was carried out to assess the levels of aflatoxin contamination in diseased peach (Prunus persica L.) fruit and their control using myco-synthesized iron oxide nanoparticles (Fe2O3 NPs). Diseased peach fruit were diagnosed to be infected with Aspergillus flavus. The isolated pathogen was cultured under UV light (365 nm) and exposed to ammonium hydroxide (31 %) vapors, which confirmed its ability to produce aflatoxin. For the control of this disease, Fe2O3 NPs were synthesized in the filtrate of a biocontrol fungus (Trichoderma harzianum) and characterized before analyzing their potential in disease control. FTIR spectrum described the presence of capping and reducing agents (secondary amines, alcohol, alkyne and aromatic compounds) on the surface of Fe2O3 NPs. X-ray Diffraction (XRD) described the crystalline size (7.78), while the spherical shape of Fe2O3 NPs was described by the SEM analysis. The EDX spectrum indicated the successful formation of Fe2O3 NPs by showing strong signals of iron (74.38 %). All concentrations displayed mycelial growth inhibition, in vitro and the greatest growth reduction (65.4 %) was observed at 1 mg/ml concentration of NPs. At the same concentration of Fe2O3 NPs, significant control of fruit rot of peach was also observed, in vivo. Treatment of Fe2O3 NPs maintained higher soluble solids, sucrose, total sugar, ascorbic acid, titratable acidity and firmness of peach fruit. Diseased fruit were further investigated for the presence and detection of aflatoxins. All three methods viz. thin layer chromatography (TLC), enzyme-linked immunosorbent assay (ELISA) and high-performance liquid chromatography (HPLC) confirmed a higher production of aflatoxins in control plants, while this production was significantly reduced in Fe2O3 NPs-treated peach fruit.

Evaluation of Bioactive Compounds and Antioxidant Activity in 51 Minor Tropical Fruits of Ecuador

Less common tropical fruits have been the subject of little research, leaving a vast field to be explored. In this context, a comprehensive study was carried out on the bioactive compounds and antioxidant capacity of 51 non-traditional fruits consumed in Ecuador. Vitamin C, organic acids, carotenoids, and phenolic compounds were evaluated using microextraction and rapid resolution liquid chromatography (RRLC) techniques, while antioxidant activity was measured using microplate readings. The results showed high levels of vitamin C (768.2 mg/100 g DW) in Dovyalis hebecarpa, total organic acids (37.2 g/100 g DW) in Passiflora tripartita, carotenoids (487.0 mg/100 g DW) in Momordica charantia, phenolic compounds (535.4 mg/g DW) in Nephelium lappaceum, Pourouma cecropiifolia (161.4 µmol TE/g DW) and Morus alba (80.5 µmol AAE/g DW) in antioxidant activity. Effective extraction of carotenoids was also observed using a mixture of methanol: acetone: dichloromethane (1:1:2) with an extraction time of 2 min, while an 80% solution of 0.1% acidified methanol with hydrochloric acid with an extraction time of 3 min was highly effective for phenolics in fruit. These results provide a valuable basis for optimising future extraction processes of bioactive compounds from non-traditional fruits, with significant implications for their potential use in various nutritional and pharmaceutical contexts.

Detection and prevention of foreign material in food: A review

This review highlights the critical concern foreign material contamination poses across the food processing industry and provides information on methods and implementations to minimize the hazards caused by foreign materials. A foreign material is defined as any non-food, foreign bodies that may cause illness or injury to the consumer and are not typically part of the food. Foreign materials can enter the food processing plant as part of the raw materials such as fruit pits, bones, or contaminants like stones, insects, soil, grit, or pieces of harvesting equipment. Over the past 20 years, foreign materials have been responsible for about one out of ten recalls of foods, with plastic fragments being the most common complaint. The goal of this paper is to further the understanding of the risks foreign materials are to consumers and the tools that could be used to minimize the risk of foreign objects in foods.

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

Applications of Delayed Luminescence for tomato fruit quality assessment across varied Sicilian cultivation zones

The food industry places significant emphasis on ensuring quality and traceability as key components of a healthy diet. To cater to consumer demands, researchers have prioritized the development of analytical techniques that can rapidly and non-invasively provide data on quality parameters. In this study, we propose to use the Delayed Luminescence (DL), an ultra-weak and photo-induced emission of optical photons, as a tool for a rapid evaluation of quality profile associated with fruit ripening, in support of traditional analysis methods. Delayed Luminescence measurements have been performed on cherry tomatoes, with and without the PGI "Pomodoro di Pachino" certification, harvested from two different growing areas of south-eastern Sicily (Italy). Then, DL emissions were correlated with soluble solid content and titratable acidity values, which are known to affect the flavor, the commerciality and the maturity degree of tomato fruits. In addition, we evaluated the changes in the DL parameters with respect to the geographical origin of the cherry tomatoes, with the aim of testing the possibility of applying the technique for identification purposes. The signals of Delayed Luminescence appeared to be good indicators of the macromolecular structure of the biological system, revealing structural changes related to the content of total soluble solids present in the juice of tomatoes analyzed, and they appeared unsuitable for authenticating vegetable crops, since the differences in the photon yields emitted by tomato Lots were not related to territory of origin. Thus, our results suggest that DL can be used as a nondestructive indicator of important parameters linked to tomato fruit quality.

Chitosan oligosaccharide as a plant immune inducer on the Passiflora spp. (passion fruit) CMV disease

Cucumber mosaic virus (CMV), one of the main viruses, is responsible for Passiflora spp. (passion fruit) virus diseases, which negatively affect its planting, cultivation, and commercial quality. In this study, a laboratory anti-CMV activity screening model for Passiflora spp. CMV disease was first established. Then, the effects of different antiviral agents of chitosan oligosaccharide (COS), dufulin (DFL), and ningnanmycin (Ning) on CMV virulence rate in Passiflora spp. were determined. The virulence rate and anti-CMV activity in Passiflora spp. treated with COS were 50% and 45.48%, respectively, which were even better than those of DFL (66.67% and 27.30%, respectively) and Ning (83.30% and 9.17%, respectively). Field trials test results showed COS revealed better average control efficiency (47.35%) against Passiflora spp. CMV disease than those of DFL (40.93%) and Ning (33.82%), indicating that COS is effective in the control of the Passiflora spp. CMV disease. Meanwhile, the nutritional quality test results showed that COS could increase the contents of soluble solids, titratable acids, vitamin C, and soluble proteins in Passiflora spp. fruits as well as enhance the polyphenol oxidase (PPO), superoxide dismutase (SOD), and peroxidase (POD) activity in the leaves of Passiflora spp. seedlings. In addition, the combined transcriptome and proteome analysis results showed that COS mainly acted on the Brassinosteroids (BRs) cell signaling pathway, one of plant hormone signal transduction pathway, in Passiflora spp., thus activating the up-regulated expression of TCH4 and CYCD3 genes to improve the resistance to CMV disease. Therefore, our study results demonstrated that COS could be used as a potential plant immune inducer to control the Passiflora spp. CMV disease in the future.

Prediction Models for the Content of Calcium, Boron and Potassium in the Fruit of 'Huangguan' Pears Established by Using Near-Infrared Spectroscopy

It has been proved that the imbalance of the proportion of elements of 'Huangguan' pears in the pulp and peel, especially calcium, boron and potassium, may be important factors that can seriously affect the pears' appearance quality and economic benefits. The objective of this study was to predict the content of calcium, boron and potassium in the pulp and peel of 'Huangguan' pears nondestructively and conveniently by using near-infrared spectroscopy (900-1700 nm) technology. Firstly, 12 algorithms were used to preprocess the original spectral data. Then, based on the original and preprocessed spectral data, full-band prediction models were established by using Partial Least Squares Regression and Gradient Boosting Regression Tree. Finally, the characteristic wavelengths were extracted by Genetic Algorithms to establish the characteristic wavelength prediction models. According to the prediction results, the value of the determination coefficient of the prediction sets of the best prediction models for the three elements all reached ideal levels, and the values of their Relative analysis error also showed high levels. Therefore, the micro near-infrared spectrometer based on machine learning can predict the content of calcium, boron and potassium in the pulp and peel of 'Huangguan' pears accurately and quickly. The results also provide an important scientific theoretical basis for further research on the degradation of the quality of 'Huangguan' pears caused by a lack of nutrients.

Application on infrared spectroscopy for the analysis of total phenolic compounds in fruits

Recent studies have demonstrated the metabolic benefits of phenolic compounds on human health. However, traditional analytical methods used for quantification of total phenolic compounds are time-consuming, laborious, require a high volume of reagents, mostly toxic substances, and involve several steps that can result in systematic and instrumental errors. Spectroscopic techniques have been used as alternatives to these methods for the determination of bioactive compounds directly in the food matrix by minimal sample preparation, without using toxic reagents. Therefore, this overview presents the advantages of nondestructive methods focusing on infrared spectroscopy (IR), for the quantification of total phenolic compounds in fruits. In addition, the main difficulties in applying these spectroscopic techniques are presented, as well as a comparison between the quantification of total phenolic compounds by traditional and IR methods. This review concludes by focusing on model building, highlighting that IR data are mainly processed using the partial least-squares (PLS) regression method to predict total phenolic content. The development of portable and inexpensive IR instruments, combined with multivariate data processing, could give to the consumers a straightforward technology to evaluate the total phenolic content of fruits prior to purchase.

Using hyperspectral imaging technology for assessing internal quality parameters of persimmon fruits during the drying process

The crucial features of persimmon are required to detect real-time moisture, water-soluble tannin, and soluble solids contents during the drying process. This study developed a method based on hyperspectral imaging (HSI) to execute online and non-destructive assaying of persimmon features. A total of 144 samples were collected, and 150 bands were scanned. The spectral data were analyzed by partial least squares regression (PLSR), principal component regression (PCR), least squares support vector regression (LS-SVR), and radial basis function neural network (RBFNN) with seven preprocessing methods. LS-SVR provided excellent performance for moisture content prediction, while PLSR was better in the analysis of water-soluble tannin and soluble solids contents. Successive projection algorithm (SPA) was used to select the optimal wavelengths to simplify the models, and about twenty important variables were chosen. Overall, these results indicate that HSI could be considered a valuable technique to quantify chemical constituents in dried persimmon fruits.

A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products

Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.

Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy

Citrus is one of the most important fruits in China. Miyagawa Satsuma, one kind of citrus, is a nutritious agricultural product with regional characteristics of Chongming Island. Near-infrared Spectroscopy (NIR) is a proper method for studying the quality of fruits, because it is low-cost, efficient, non-destructive, and repeatable. Therefore, the NIR technique is used to detect citrus's soluble solid content (SSC) in this study. After obtaining the original spectral data, the first 70% of them are divided into the training set and 30% into the test set. Then, the Random Frog algorithm is chosen to select characteristic wavelengths, which reduces the dimension of the data and the complexity of the model, and accordingly makes the generalization of the classification model better. After comparing the performance of various classifiers (AdaBoost, KNN, LS-SVM, and Bayes) under different characteristic wavelength numbers, the AdaBoost classifier outperforms using 275 characteristic wavelengths for modeling eventually. The accuracy, precision, recall, and F ₁-score are 78.3%, 80.5%, 78.3%, and 0.780, respectively and the ROC (Receiver Operating Characteristic Curve, ROC curve) is close to the upper left corner, suggesting that the classification model is acceptable. The results demonstrate that it is feasible to use the NIR technique to estimate whether the citrus is sweet or not. Furthermore, it is beneficial for us to apply the obtained models for identifying the quality of citrus correctly. For fruit traders, the model helps them to determine the growth cycle of citrus more scientifically, improve the level of citrus cultivation and management and the final fruit quality, and thus increase the economic income of fruit traders.

Raman Method in Identification of Species and Varieties, Assessment of Plant Maturity and Crop Quality—A Review

The present review covers reports discussing potential applications of the specificity of Raman techniques in the advancement of digital farming, in line with an assumption of yield maximisation with minimum environmental impact of agriculture. Raman is an optical spectroscopy method which can be used to perform immediate, label-free detection and quantification of key compounds without destroying the sample. The authors particularly focused on the reports discussing the use of Raman spectroscopy in monitoring the physiological status of plants, assessing crop maturity and quality, plant pathology and ripening, and identifying plant species and their varieties. In recent years, research reports have presented evidence confirming the effectiveness of Raman spectroscopy in identifying biotic and abiotic stresses in plants as well as in phenotyping and digital selection of plants in farming. Raman techniques used in precision agriculture can significantly improve capacities for farming management, crop quality assessment, as well as biological and chemical contaminant detection, thereby contributing to food safety as well as the productivity and profitability of agriculture. This review aims to increase the awareness of the growing potential of Raman spectroscopy in agriculture among plant breeders, geneticists, farmers and engineers.

Determination of the Soluble Solids Content in Korla Fragrant Pears Based on Visible and Near-Infrared Spectroscopy Combined With Model Analysis and Variable Selection

The non-destructive detection of soluble solids content (SSC) in fruit by near-infrared (NIR) spectroscopy has a good application prospect. At present, the application of portable devices is more common. The construction of an accurate and stable prediction model is the key for the successful application of the device. In this study, the visible and near-infrared (Vis/NIR) spectra of Korla fragrant pears were collected by a commercial portable measurement device. Different pretreatment methods were used to preprocess the raw spectra, and the partial least squares (PLS) model was constructed to predict the SSC of pears for the determination of the appropriate pretreatment method. Subsequently, PLS and least squares support vector machine (LS-SVM) models were constructed based on the preprocessed full spectra. A new combination (BOSS-SPA) of bootstrapping soft shrinkage (BOSS) and successive projections algorithm (SPA) was used for variable selection. For comparison, single BOSS and SPA were also used for variable selection. Finally, three types of models, namely, PLS, LS-SVM, and multiple linear regression (MLR), were constructed based on different input variables. Comparing the prediction performance of all models, it showed that the BOSS-SPA-PLS model based on 17 variables obtained the best SSC assessment ability with r _p of 0.94 and RMSEP of 0.27 °Brix. The overall result indicated that portable measurement with Vis/NIR spectroscopy can be used for the detection of SSC in Korla fragrant pears.

Nutraceutical Quality and Soluble Solids of Cucumis melo Developed in Vermicompost-River Sand Mixtures, Under Shade Mesh Conditions

<b>Background and Objective:</b> There is ample evidence that the consumption of fruits and vegetables, with a high content of secondary metabolites, favours better human health. In the same sense, the application of organic fertilizers has favoured the content of phenolic compounds and a greater antioxidant capacity in various crops. Factoring that the application of Vermicompost (VC) increases the nutritional quality of plants. This research determined the content of soluble solids, biochemical components and the antioxidant capacity of the melon fruits (<i>Cucumis melo</i> L.) developed under shade net conditions with VC as a source of fertilization. <b>Materials and Methods:</b> During the 2020 spring-summer season, a hybrid melon Honeydew type was grown under five mixtures of VC: River sand (RS), with ratios 0:1, 1:1, 1:2, 1:3 and 1:4. Nutrient Steiner solution was applied only in the first mixture (control) while VC was used as an organic nutrient for all other mixtures. The five formulated mixtures, with five replications, were randomly distributed. The total phenolic content, total flavonoids, antioxidant capacity and the total soluble solids of the melon fruit were determined. Data obtained were statistically analyzed by analysis of variance and means were compared by Tukey 0.05 test. <b>Results:</b> The ANOVA indicates that no statistical differences were registered for any of the variables under study, however, the levels of the parameters were high. <b>Conclusion:</b> VC treatment of melon fruit is proven to be a promising source of nutrients to plants increasing their natural antioxidants and content of soluble solids.

Ni2+-Doped Garnet Solid-Solution Phosphor-Converted Broadband Shortwave Infrared Light-Emitting Diodes toward Spectroscopy Application

Broadband shortwave infrared (SWIR) light-emitting diodes (LEDs), capable of advancing the next-generation solid-state smart invisible lighting technology, have sparked tremendous interest and will launch ground-breaking spectroscopy and instrumental applications. Nevertheless, the device performance is still suppressed by the low quantum efficiency and limited emission bandwidth of the critical phosphor layer. Herein, we report a high-performance Ni²⁺-doped garnet solid-solution broadband SWIR emitter centered at ∼1450 nm with a large full-width at half-maximum of ∼300 nm, thereby fabricating, for the first time, a directly excited Ni²⁺-doped garnet solid-solution phosphor-converted broadband SWIR LED device. A synergetic enhancement strategy, adding a fluxing agent and a charge compensator simultaneously, is proposed to deliver a more than 20-fold increase of the SWIR emission intensity and nearly 2-fold improvement of the thermal quenching behavior. The site occupation and mechanism behind the synergetic enhancement strategy are elucidated by a combination of experimental study and theoretical calculation. A prototype of the SWIR LED with a radiation flux of 1.25 mW is fabricated and utilized as an invisible SWIR light source to demonstrate the SWIR spectroscopy applications. This work not only opens a window to explore novel broadband SWIR phosphors but also provides a synergetic strategy to remarkably improve the performance of artificial SWIR LED light sources.

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

Advances in differentiation and identification of foodborne bacteria using near infrared spectroscopy

Rapid and sensitive detection of foodborne bacteria is a growing concern for ensuring safe food supply and preventing human foodborne infections. It is difficult for conventional methods to meet these detection requirements because they are often tedious and time-consuming. In the recent years, near infrared (NIR) spectroscopy has been found to be a promising method for all sorts of analyses in microbiology due to its highly specific absorption signature and non-destructive measurements. In this review, we first briefly introduce the fundamental and basic operational procedure of NIR spectroscopy for foodborne bacteria detection. Then we summarize the main advances and contributions of this technique in the study of foodborne bacteria. Finally, we conclude that much work still remains to be done before NIR spectroscopy really becomes a viable alternative in the field of microbiological characterization.

Internal quality evaluation of chestnut using nuclear magnetic resonance

This study was carried out to develop a prediction model for soluble solid content (SSC) of intact chestnut and to detect internal defects using nuclear magnetic resonance (NMR) relaxometry and magnetic resonance imaging (MRI). Inversion recovery and Carr–Purcell–Meiboom–Gill (CPMG) pulse sequences used to determine the longitudinal (T1) and transverse (T2) relaxation times, respectively. Partial least squares regression (PLSR) was adopted to predict SSCs of chestnuts with NMR data and histograms from MR images. The coefficient of determination (R 2), root mean square error of prediction (RMSEP), ratio of prediction to deviation (RPD), and the ratio of error range (RER) of the optimized model to predict SSC were 0.77, 1.41 °Brix, 1.86, and 11.31 with a validation set. Furthermore, an image-processing algorithm has been developed to detect internal defects such as decay, mold, and cavity using MR images. The classification applied with the developed image processing algorithm was over 94% accurate to classify. Based on the results obtained, it was determined that the NMR signal could be applied for grading several levels by SSC, and MRI could be used to evaluate the internal qualities of chestnuts.

Feasibility of rapid piperine quantification in whole and black pepper using near infrared spectroscopy and chemometrics

Piperine is a bioactive alkaloid that possesses various health benefits and is responsible for the pungent aroma of pepper. Piperine content in whole and ground black pepper (n = 132) was analyzed by near-infrared spectroscopy (NIRS) in the 950 to 1650 nm wavelength window. Chemometric modeling using partial least square regression was performed, and outliers were checked and removed during the preparation of the calibration curve by considering sample residual variance and sample leverage. Model accuracy was evaluated with a low root-mean-square error of cross-validation (RMSECV) and a high ratio performance to deviation (RPD). The optimal model had a coefficient of determination (R² ) of 0.726, RMSECV of 0.289 g/100 g, and RPD of 1.744 for the whole black pepper. The results of R² , RMSECV, and RPD for the ground black pepper were 0.850, 0.231 g/100 g, and 2.424, respectively. Therefore, based on the perspective of onsite process, the proposed NIRS method can be employed for selecting abnormal samples during the inspection of black pepper raw material and for quantifying piperine contents of final black pepper product. PRACTICAL APPLICATION: Generally, the quality indicators of black pepper are graded solely based on their external appearance, quality, and size. This study discloses the development of a near-infrared spectroscopy-based fast and accurate nondestructive analytical method for the detection of piperine, a bioactive constituent of pepper, as a tool for the quality control of whole and ground black pepper.

Nondestructive monitoring storage quality of apples at different temperatures by near-infrared transmittance spectroscopy

Apple is the most widely planted fruit in the world and is popular in consumers because of its rich nutritional value. In this study, the portable near-infrared (NIR) transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. The postharvest quality of apples including soluble solids content (SSC), vitamin C (VC), titratable acid (TA), and firmness was evaluated, and the portable spectrometer was used to obtain near-infrared transmittance spectra of apples in the wavelength range of 590-1,200 nm. Mixed temperature compensation method (MTC) was used to reduce the influence of temperature on the models and to improve the adaptability of the models. Then, variable selection methods, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were developed to improve the performance of the models by determining characteristic variables and reducing redundancy. Comparing the full spectral models with the models established on variables selected by different variable selection methods, the CARS combined with partial least squares (PLS) showed the best performance with prediction correlation coefficient (R p) and residual predictive deviation (RPD) values of 0.9236, 2.604 for SSC; 0.8684, 2.002 for TA; 0.8922, 2.087 for VC; and 0.8207, 1.992 for firmness, respectively. Results showed that NIR transmittance spectroscopy was feasible to detect postharvest quality of apples during storage.

Advanced Analysis of Roots and Tubers by Hyperspectral Techniques

Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.

Prediction of Firmness and pH for "Golden Delicious" Apple Based on Elasticity Index from Modal Analysis

An experimental modal test system was established to extract the natural frequencies of "Golden Delicious" apple, after which the elasticity index was calculated to predict the apple quality parameters based on the orthogonal polynomials method. The elasticity index in every vibration mode changed dramatically (P = 0.01) along time revolution. The multivariate regression methods were used to model the predictive relationship between the elasticity index and the apple quality parameters. The models of the apple juice pH based on support vector regression presented adequate determination coefficients of calibration set (Q²  = 0.68) and prediction set (R²  = 0.55), respectively. The models based on partial least squares regression could be used for predicting the apple firmness parameter offset gradient (Q²  = 0.76 and R²  = 0.72). It helped understanding the fruit dynamic properties of the fruit and spontaneously obtaining the fruit chemical parameters. A nondestructive and portable device was viable for fruit quality estimation by the modal test system during storage, transport, and even growth on the tree.

PRACTICAL APPLICATION

A nondestructive and portable device was provided for fruit quality detection during storage, transport and even growth based on experimental modal analysis. A systematic statistical analysis method about outlier detection, data set partitioning, parameter optimization, and multiple regression models were provided.