Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network

BACKGROUND

Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions.

RESULTS

This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance.

CONCLUSION

The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.

Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering

The maturity of kiwifruit is widely gauged by its soluble solids content (SSC), with accurate assessment being essential to guarantee the fruit's quality. Hyperspectral imaging offers a non-destructive alternative to traditional destructive methods for SSC evaluation, though its efficacy is often hindered by the redundancy and external disturbances of spectral images. This study aims to enhance the accuracy of SSC predictions by employing feature engineering to meticulously select optimal spectral features and mitigate disturbance effects. We conducted a comprehensive investigation of four spectral pre-processing and nine spectral feature selection methods, as components of feature engineering, to determine their influence on the performance of a linear regression model based on ordinary least squares (OLS). Additionally, the stacking generalization technique was employed to amalgamate the strengths of the two most effective models derived from feature engineering. Our findings demonstrate a considerable improvement in SSC prediction accuracy post feature engineering. The most effective model, when considering both feature engineering and stacking generalization, achieved an R M S E p of 0.721, a M A P E p of 0.046, and an R P D p of 1.394 in the prediction set. The study confirms that feature engineering, especially the careful selection of spectral features, and the stacking generalization technique are instrumental in bolstering SSC prediction in kiwifruit. This advancement enhances the application of hyperspectral imaging for quality assessment, offering benefits that extend across the agricultural industry.

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy

Introduction

Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming.

Methods

To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models.

Results

The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix).

Discussion

This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

QTL Mapping and Genome-Wide Association Study Reveal Genetic Loci and Candidate Genes Related to Soluble Solids Content in Melon

Melon (Cucumis melo L.) is an economically important Cucurbitaceae crop grown around the globe. The sweetness of melon is a significant factor in fruit quality and consumer appeal, and the soluble solids content (SSC) is a key index of melon sweetness. In this study, 146 recombinant inbred lines (RILs) derived from two oriental melon materials with different levels of sweetness containing 1427 bin markers, and 213 melon accessions containing 1,681,775 single nucleotide polymorphism (SNP) markers were used to identify genomic regions influencing SSC. Linkage mapping detected 10 quantitative trait loci (QTLs) distributed on six chromosomes, seven of which were overlapped with the reported QTLs. A total of 211 significant SNPs were identified by genome-wide association study (GWAS), 138 of which overlapped with the reported QTLs. Two new stable, co-localized regions on chromosome 3 were identified by QTL mapping and GWAS across multiple environments, which explained large phenotypic variance. Five candidate genes related to SSC were identified by QTL mapping, GWAS, and qRT-PCR, two of which were involved in hydrolysis of raffinose and sucrose located in the new stable loci. The other three candidate genes were involved in raffinose synthesis, sugar transport, and production of substrate for sugar synthesis. The genomic regions and candidate genes will be helpful for molecular breeding programs and elucidating the mechanisms of sugar accumulation.

Fine Mapping of fw6.3, a Major-Effect Quantitative Trait Locus That Controls Fruit Weight in Tomato

Tomato (Solanum lycopersicum) is a widely consumed vegetable, and the tomato fruit weight is a key yield component. Many quantitative trait loci (QTLs) controlling tomato fruit weight have been identified, and six of them have been fine-mapped and cloned. Here, four loci controlling tomato fruit weight were identified in an F₂ population through QTL seq.; fruit weight 6.3 (fw6.3) was a major-effect QTL and its percentage of variation explanation (R²) was 0.118. This QTL was fine-mapped to a 62.6 kb interval on chromosome 6. According to the annotated tomato genome (version SL4.0, annotation ITAG4.0), this interval contained seven genes, including Solyc06g074350 (the SELF-PRUNING gene), which was likely the candidate gene underlying variation in fruit weight. The SELF-PRUNING gene contained a single-nucleotide polymorphism that resulted in an amino acid substitution in the protein sequence. The large-fruit allele of fw6.3 (fw6.3_HG) was overdominant to the small-fruit allele fw6.3_RG. The soluble solids content was also increased by fw6.3_HG. These findings provide valuable information that will aid the cloning of the FW6.3 gene and ongoing efforts to breed tomato plants with higher yield and quality via molecular marker-assisted selection.

Performance of Sweet Cherry Cultivars and Advanced Selections on Gisela 5 Rootstock in Young Orchards

Six sweet cherry cultivars and two advanced selections of Gisela 5 rootstock were tested in 2015-2021 at the Institute of Horticulture, Lithuanian Research Centre for Agriculture and Forestry. Fruit trees were planted at distances of 4.5 × 2.5 m and trained as spindles. Orchard floor management included frequently mown grass in alleyways with herbicide strips along tree rows. Cultivars 'Mindaugė' and 'Irema BS' were the most vigorous at the end of the seventh leaf. Their trunk diameter achieved 11.6 cm. The 'Merchant' cultivar had the smallest trunk diameter-9.3 cm. The average yield in 2018-2021 ranged from 2.75 t/ha for 'Vega' to 8.73 t/ha for 'Regina'. Cultivars 'Regina', 'Sunburst', 'Irema BS' and 'Merchant' had the highest cumulative yield efficiency of 0.440-0.503 kg/cm² with respect to the trunk cross-section area (TCSA). The least productive cultivar 'Vega' produced fruits of the highest average weight-9.9 g. Fruits of 'Regina' and 'Sunburst' were large as well-8.8-9.1 g. 'Irema BS' fruits had the highest soluble solids content (SSC)-20.2%. The lowest SSC was recorded in 'Merchant' and 'Sunburst' fruits-14.7-15.8%. The yield of advanced selection, No. 102, equaled to the yield of cv. 'Regina'. No. 102 had a high fruit weight, and fruits were distinguished by attractiveness and taste.

Hyperspectral Imaging with Machine Learning Approaches for Assessing Soluble Solids Content of Tribute Citru

Tribute Citru is a natural citrus hybrid with plenty of vitamins and nutrients. Fruits' soluble solids content (SSC) is a critical quality index. This study used hyperspectral imaging at two spectral ranges (400-1000 nm and 900-1700 nm) to determine SSC in Tribute Citru. Partial least squares regression (PLSR) and support vector regression (SVR) models were established in order to determine SSC using the spectral information of the calyx and blossom ends. The average spectra of both ends as well as their fusion was studied. The successive projections algorithm (SPA) and the correlation coefficient analysis (CCA) were used to examine the differences in characteristic wavelengths between the two ends. Most models achieved performances with the correlation coefficient of the training, validation, and testing sets over 0.6. Results showed that differences in the performances among the models using the one-sided and two-sided spectral information. No particular regulation could be found for the differences in model performances and characteristic wavelengths. The results illustrated that the sampling side was an influencing factor but not the determinant factor for SSC determination. These results would help with the development of real-world applications for citrus quality inspection without concerning the sampling sides and the spectral ranges.

Internal Quality Attributes and Sensory Characteristics of 'Ambrosia' Apples with Different Dry Matter Content after a Two-Week and a Ten-Week Air Storage at 1 °C

This research was conducted to determine the compositional and textural characteristics and sensory profile of 'Ambrosia' apples with different dry matter content (DMC) as estimated using a Felix-750 Produce Quality Meter (Felix Instruments Inc., Camas, WA, USA). Fruits were harvested from a commercial orchard in Cawston and an experimental field in Summerland Research and Development Centre (SuRDC) in British Columbia, Canada, when the average absorbance difference index/coefficient of fruit skin δAbsorbance (δA) dropped under 0.45 ± 0.10. DMC levels were estimated after harvest at the blush/background transition zone for fruit categorization on 300 fruits from each location. Fruits were coded with an individual number and grouped in different DMC categories. The distribution of the estimated DMC levels obtained from two locations was different. The results indicate that DMC levels were strongly and positively correlated with the soluble solids content (SSC) of the fruit (r = 0.81). Sensory evaluations also demonstrated that apples in the lowest DMC category (12.5% ± 0.5 from Cawston) were considered the least sweet apples with the least overall flavour quality by panellists compared to the apples from the other DMC categories included in the sensory evaluations from the two locations. Panellists also perceived less-than-expected "fresh apple" and "tropical" flavours but more-than-expected "no flavour" and "bland" off flavour from the lowest-DMC-category apples. The non-destructive DMC measurements show a potential to be used to sort apples for SSC, sweetness and flavour; nevertheless, they were not related to firmness or textural attributes.

Soil Amendment and Storage Effect the Quality of Winter Melons (Benincasa hispida (Thunb) Cogn.) and Their Juice

Winter melon fruits were grown in the field using anaerobic soil disinfestation (ASD) and conventional fertilizer alone as the control treatment. Fruits were harvested and stored at 20 °C for 120 d, the juice was processed on day one and day 120, and the effects of soil amendment and 120 d storage on the juice's physical and chemical (sugars, acids, volatile and nutritional compounds) properties were evaluated. Fruit juice extracted from ASD-grown fruit had greater magnitude of zeta potential than the control juice, indicating it was physically more stable than the juice obtained from the control conditions. ASD fruit juice had lower soluble solids content (SSC), and lower volatile compounds that contribute green, grass, and sulfur notes, and negatively influence flavor quality. ASD fruit juice had higher vitamin B5 and cytidine. Juice processed from 120 d stored fruit had less yield due to 12.4-15.6% weight loss. The non-soluble solids content was higher and particle size was larger, and the SSC and individual sugars decreased. However, titratable acidity (TA) increased primarily due to increased citric acid. Out of 16 free amino acids, 6 increased and only 1 decreased. However, three out of five nucleosides decreased; vitamins B1 and B6 increased; vitamins B2, B3 and C decreased. Overall, juice derived from fruit produced using ASD was physically more stable and had less SSC and off-odor volatiles than the control, while the fruit juice of those stored for 120 d had lower SSC and higher TA and nutritional profiles, comparable to freshly harvested fruit.

Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation

Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and the corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with the spectrum ranging from 400 to 1,000 nm. The acquired hyperspectral images are corrected and the spectral information are extracted. A novel one-dimensional (1D) convolutional ResNet (Con1dResNet) based regression model is proposed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4% better than state of art technique for SSC and 33.7% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future.

Correction of Temperature Variation with Independent Water Samples to Predict Soluble Solids Content of Kiwifruit Juice Using NIR Spectroscopy

Using the framework of aquaphotomics, we have sought to understand the changes within the water structure of kiwifruit juice occurring with changes in temperature. The study focuses on the first (1300-1600 nm) and second (870-1100 nm) overtone regions of the OH stretch of water and examines temperature differences between 20, 25, and 30 °C. Spectral data were collected using a Fourier transform-near-infrared spectrometer with 1 mm and 10 mm transmission cells for measurements in the first and second overtone region, respectively. Water wavelengths affected by temperature variation were identified. Aquagrams (water spectral patterns) highlight slightly different responses in the first and second overtone regions. The influence of increasing temperature on the peak absorbance of the juice was largely a lateral wavelength shift in the first overtone region and a vertical amplitude shift in the second overtone region of water. With the same data set, we investigated the use of external parameter orthogonalisation (EPO) and extended multiple scatter correction (EMSC) pre-processing to assist in building temperature-independent partial least square regression models for predicting soluble solids concentration (SSC) of kiwifruit juice. The interference component selected for correction was the first principal component loading measured using pure water samples taken at the same three temperatures (20, 25, and 30 °C). The results show that the EMSC method reduced SSC prediction bias from 0.77 to 0.1 °Brix in the first overtone region of water. Using the EPO method significantly reduced the prediction bias from 0.51 to 0.04 °Brix, when applying a model made at one temperature (30 °C) to measurements made at another temperature (20 °C) in the second overtone region of water.

How Do Consumers Perceive Sensory Attributes of Apple?

Pomological characteristics and consumer acceptability of four scab-resistant apple cultivars ('Topaz', 'Florina', 'Goldstar' and 'Golden Orange') and standard commercial cultivar 'Golden Delicious' were investigated. Consumer acceptability consisted of rating fruit samples on Likert scales measuring appearance, flavour, size, sweetness, acidity, crispiness, juiciness, skin texture and general impression. Consumers better evaluated the cultivar 'Topaz' sensory characteristics of flavour, juiciness, taste and general impression than other evaluated scab-resistant apple cultivars and the cultivar 'Golden Delicious'. 'Golden Delicious' got good grades for appearance, size and sweetness. 'Topaz' also had the best pomological characteristic related to measured fruit firmness, contents of soluble solids and organic acids. It can be concluded that only the cultivar 'Topaz' among the scab-resistant apple cultivars achieved a good consumer assessment.

QTL Mapping and GWAS Reveal the Genetic Mechanism Controlling Soluble Solids Content in Brassica napus Shoots

Oilseed-vegetable-dual-purpose (OVDP) rapeseed can effectively alleviate the land contradiction between crops and it supplements vegetable supplies in winter or spring. The soluble solids content (SSC) is an important index that is used to evaluate the quality and sugar content of fruits and vegetables. However, the genetic architecture underlying the SSC in Brassica napus shoots is still unclear. Here, quantitative trait loci (QTLs) for the SSC in B. napus shoots were investigated by performing linkage mapping using a recombinant inbred line population containing 189 lines. A germplasm set comprising 302 accessions was also used to conduct a genome-wide association study (GWAS). The QTL mapping revealed six QTLs located on chromosomes A01, A04, A08, and A09 in two experiments. Among them, two major QTLs, qSSC/21GY.A04-1 and qSSC/21NJ.A08-1, accounted for 12.92% and 10.18% of the phenotypic variance, respectively. In addition, eight single-nucleotide polymorphisms with phenotypic variances between 5.62% and 10.18% were identified by the GWAS method. However, no locus was simultaneously identified by QTL mapping and GWAS. We identified AH174 (7.55 °Brix and 7.9 °Brix), L166 (8.9 °Brix and 8.38 °Brix), and L380 (8.9 °Brix and 7.74 °Brix) accessions can be used as superior parents. These results provide valuable information that increases our understanding of the genetic control of SSC and will facilitate the breeding of high-SSC B. napus shoots.

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R ²) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

A Portable Spectrometric System for Quantitative Prediction of the Soluble Solids Content of Apples with a Pre-calibrated Multispectral Sensor Chipset

A portable spectrometric system for nondestructive assessment of the soluble solids content (SSC) of fruits for practical applications has been proposed and its performance has been examined by an experiment on quantitative prediction of the SSC of apples. Although the spectroscopic technique is a powerful tool for predicting the internal qualities of fruits, its practical applications are limited due to its high cost and complexity. In the proposed system, the spectra of apples were collected by a simple optical setup with a cheap pre-calibrated multispectral chipset. An optimal multiple linear regression model with five wavebands at 900, 760, 730, 680, and 535 nm revealed the best performance with the coefficient of determination of prediction and the root mean square error of prediction of 0.861 and 0.403 °Brix, respectively, which was comparable to that of the previous studies using dispersive spectrometers. Compared with previously reported systems using discrete filters or light emitting diodes, the proposed system was superior in terms of manufacturability and reproducibility. The experimental results confirmed that the proposed system had a considerable potential for practical, cost-effective applications of the SSC prediction, not only for apples but also for other fruits.

Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R² = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.

A Comparative Study of PLSR and SVM-R with Various Preprocessing Techniques for the Quantitative Determination of Soluble Solids Content of Hardy Kiwi Fruit by a Portable Vis/NIR Spectrometer

Linear partial least square and non-linear support vector machine regression analysis with various preprocessing techniques and their combinations were used to determine the soluble solids content of hardy kiwi fruits by a handheld, portable near-infrared spectroscopy. Fruits of four species, namely Autumn sense (A), Chungsan (C), Daesung (D), and Green ball (Gb) were collected from five different areas of Gwangyang (G), Muju (M), Suwon (S), Wonju (Q), and Yeongwol (Y) in South Korea. The dataset for calibration and prediction was prepared based on each area, species, and in combination. Half of the dataset of each area, species, and combined dataset was used as calibrated data and the rest was used for model validation. The best prediction correlation coefficient ranges between 0.67 and 0.75, 0.61 and 0.77, and 0.68 for the area, species, combined dataset, respectively using partial least square regression (PLSR) method with different preprocessing techniques. On the other hand, the best correlation coefficient of predictions using the support vector machine regression (SVM-R) algorithm was 0.68 and 0.80, 0.62 and 0.79, and 0.74 for the area, species, and combined dataset, respectively. In most cases, the SVM-R algorithm produced better results with Autoscale preprocessing except G area and species Gb, whereas the PLS algorithm shows a significant difference in calibration and prediction models for different preprocessing techniques. Therefore, the SVM-R method was superior to the PLSR method in predicting soluble solids content of hardy kiwi fruits and non-linear models may be a better alternative to monitor soluble solids content of fruits. The finding of this research can be used as a reference for the prediction of hardy kiwi fruits soluble solids content as well as harvesting time with better prediction models.

Non-Destructive Soluble Solids Content Determination for 'Rocha' Pear Based on VIS-SWNIR Spectroscopy under 'Real World' Sorting Facility Conditions

In this paper we report a method to determine the soluble solids content (SSC) of 'Rocha' pear (Pyrus communis L. cv. Rocha) based on their short-wave NIR reflectance spectra (500-1100 nm) measured in conditions similar to those found in packinghouse fruit sorting facilities. We obtained 3300 reflectance spectra from pears acquired from different lots, producers and with diverse storage times and ripening stages. The macroscopic properties of the pears, such as size, temperature and SSC were measured under controlled laboratory conditions. For the spectral analysis, we implemented a computational pipeline that incorporates multiple pre-processing techniques including a feature selection procedure, various multivariate regression models and three different validation strategies. This benchmark allowed us to find the best model/preproccesing procedure for SSC prediction from our data. From the several calibration models tested, we have found that Support Vector Machines provides the best predictions metrics with an RMSEP of around 0.82 ∘ Brix and 1.09 ∘ Brix for internal and external validation strategies respectively. The latter validation was implemented to assess the prediction accuracy of this calibration method under more 'real world-like' conditions. We also show that incorporating information about the fruit temperature and size to the calibration models improves SSC predictability. Our results indicate that the methodology presented here could be implemented in existing packinghouse facilities for single fruit SSC characterization.

Non-destructive determination of strawberry fruit and juice quality parameters using ultraviolet, visible, and near-infrared spectroscopy

BACKGROUND

The development of rapid methods for the determination of the soluble solids content (SSC) and total phenolic content (TPC) in fruit juices is of great interest. Soluble solids content is related to sensory attributes, whereas TPC is related to the antioxidant capacity of juices. The aim of this study was to develop and optimize the calibration models for the prediction of the SSC and TPC of strawberry juices from the spectra of fruit and juices.

RESULTS

Near infrared (NIR) spectra were measured for strawberry fruit and ultraviolet (UV), visible (VIS), and NIR spectra were measured for juices. The partial least squares regression models were validated using the test sample set and their predictive ability was evaluated on the basis of determination coefficients (R² _P ) and root mean square error of prediction (RMSEP). For SSC the models with high predictive ability were obtained using spectra of fruit (R² _P = 0.929, RMSEP = 0.46%) or juices (R² _P = 0.979, RMSEP = 0.25%) in the NIR range. The optimal models for TPC were obtained using NIR spectra of fruit (R² _P = 0.834, RMSEP = 130.8 mg GA L^-1 ) or UV-VIS-NIR spectra of juices (R² _P = 0.844, RMSEP = 126.7 mg GA L^-1 ).

CONCLUSION

The results show the potential of spectroscopy for predicting quality parameters of strawberry juices from the juice spectra itself or non-destructively from the fruit spectra. They may contribute to the development of fruit sorting systems to optimize their use in juice production, as well as fast-screening methods for quality control of juices. © 2019 Society of Chemical Industry.

P-Wave Visible-Shortwave-Near-Infrared (Vis-SW-NIR) Detection System for the Prediction of Soluble Solids Content and Firmness on Wax Apples

A nondestructive system for measuring the soluble solids content (SSC) and firmness of wax apples was developed using 670, 850, 880, 940, and 980 nm visible-shortwave-near-infrared (Vis-SW-NIR) light-emitting diode (LED) light sources and a silicon (Si) photodetector. These specified wavelengths are highly correlated with the SSC and the firmness of fruit. An LED light source was incident onto the fruit as parallel-polarized waves (P-wave) at the Brewster angle (θ_B) to minimize the interfacial reflection and maximize the C-H and O-H bonds absorption signals from the fruit. Partial least squares (PLS) regression is used to build calibration modes and analyze the prediction of the correlation (rp2) and the root mean square error for prediction (RMSEP) of the reflected optical signals with SSC and firmness. This resulted in rp2 and RMSEP values of 0.87 and 0.66 °Bx, respectively, in SSC measurements and 0.80 and 1.16 N/cm², respectively, in firmness measurements. Therefore, the result shows rp2 of SSC and firmness are 6.4% and 9% higher and the RMSEP are 14% and 20% lower, respectively, than those obtained using non-polarized LED light sources.

Oviposition Suitability of Drosophila Suzukii (Diptera: Drosophilidae) for Nectarine Varieties and Its Correlation with the Physiological Indexes

The nectarine is an important fruit, which is attacked by Drosophila suzukii in Europe and the United States but there are no reports of it attacking nectarines in China. Here, we determined the oviposition preference of D. suzukii six on intact and sliced nectarine varieties in China and how physical and physiological indexes of the fruit correlate with these preferences. D. suzukii were allowed to oviposit on two early–, two middle– and two late–maturing varieties of nectarine—Shuguang and Chunguang, Fengguang and Zhong you 4, Zhong you 7 and Zhong you 8, respectively and the number of larvae also followed the order. The firmness, soluble solids content and the nutritional components of the amino acid, protein, soluble sugar and pectin contents of each variety were measured. D. suzukii preferred the early Shuguang variety, followed by the early Chunguang variety and then the middle Zhong you 4 and Fengguang varieties. Taken together, results show that D. suzukii shows preferences for earlier rather than later varieties of nectarines in China and that these preferences are related to the fruit’s physical and physiological traits. Results suggest that mixed cultivation of early–, middle– and late–maturing nectarine varieties should be avoided in order to prevent fly dispersal and infestation by D. suzukii.

Fruit quality of seedless watermelon grafted onto squash rootstocks under different production systems

BACKGROUND

The market demand for seedless watermelon has been continuously increasing because of consumer preference. Grafting is a useful tool to manage soilborne diseases in watermelon production, but the use of squash rootstocks may negatively affect watermelon fruit quality. Currently, most research has focused on seeded cultivars, while grafting effects on seedless watermelons remain largely unknown. This multi-season study was conducted to assess the effects of squash rootstocks, including both Cucurbita maxima × C. moschata and C. moschata cultivars, with intact or excised and regenerated roots, on fruit quality of seedless watermelon 'Melody' using both instrumental and sensory measurements under different production scenarios. The grafted watermelon plants were also challenged by field inoculation with Fusarium oxysporum f.sp. niveum.

RESULTS

A combination of instrumental measurements and consumer sensory analyses suggested that fruit quality of the seedless watermelon 'Melody' was not impacted by the use of the squash rootstocks used in this study, which included soluble solids content, titratable acidity, pH and most fruit sensory properties. Watermelon flesh firmness was increased by grafting but the grafting effect on lycopene content was inconclusive. Root excision and regeneration did not influence the grafting effect, whereas the grafting effect on flesh firmness varied among the rootstocks under Fusarium inoculation.

CONCLUSION

Overall, grafting with squash rootstocks did not reduce fruit quality attributes of 'Melody' but improved texture. Our results support incorporating grafting into integrated management programs for seedless watermelon production. © 2017 Society of Chemical Industry.

Influence of Sampling Component on Determination of Soluble Solids Content of Fuji Apple Using Near-Infrared Spectroscopy

Fuji apples from two production areas were separated into six batches by different experimenters. After applying light (500-1010 nm) on the surface of intact ones for their visible and near-infrared (NIR) spectra, destructive samples of three apple components were taken to determine the soluble solids content (SSC). Correlation and regression coefficients between the second Savitzky-Golay derivative of the spectra and SSC were analyzed to reveal that SSC values derived from the different apple components showed significantly different responses in the visible region. However, similar responses, particularly in the NIR section (730-932 nm), remained, including two sugar bands at 890 and 906 nm. On the basis of applying above characteristic bands to remove the interference signals, partial least square (PLS) and multiple linear regression (MLR) showed similar effective performances. According to the analysis of variance (ANOVA) method, sampling methods had significant effect on quantitative accuracy, and the model, using SSC values detected from the outer flesh cuboid (2.5 × 2.5 × 1.5 cm³), provided the best performance with lower root mean square error of prediction and higher correlation coefficient.

Rapid monitoring of grape withering using visible near-infrared spectroscopy

BACKGROUND

Wineries need new practical and quick instruments, non-destructive and able to quantitatively evaluate during withering the parameters that impact product quality. The aim of the work was to test an optical portable system (visible near-infrared (NIR) spectrophotometer) in a wavelength range of 400-1000 nm for the prediction of quality parameters of grape berries during withering.

RESULTS

A total of 300 red grape samples (Vitis vinifera L., Corvina cultivar) harvested in vintage year 2012 from the Valpolicella area (Verona, Italy) were analyzed. Qualitative (principal component analysis, PCA) and quantitative (partial least squares regression algorithm, PLS) evaluations were performed on grape spectra. PCA showed a clear sample grouping for the different withering stages. PLS models gave encouraging predictive capabilities for soluble solids content (R(2) val  = 0.62 and ratio performance deviation, RPD = 1.87) and firmness (R(2) val  = 0.56 and RPD = 1.79).

CONCLUSION

The work demonstrated the applicability of visible NIR spectroscopy as a rapid technique for the analysis of grape quality directly in barns, during withering. The sector could be provided with simple and inexpensive optical systems that could be used to monitor the withering degree of grape for better management of the wine production process.

Instrumental and sensory analyses of quality attributes of grafted specialty melons

BACKGROUND

Soilborne disease management remains a great challenge in melon production with the phaseout of soil fumigant methyl bromide. Grafting has been shown to be an effective approach to control soilborne diseases. However, previous research has yielded mixed results regarding the impacts of rootstock on fruit quality. Very few studies have assessed melon quality attributes using both sensory evaluation and instrumental methods.

RESULTS

Galia melon 'Arava' (Cucumis melo L. var. reticulatus Ser.) and honeydew melon 'Honey Yellow' (C. melo L. var. inodorus Naud.) were grafted onto commercial hybrid squash (Cucurbita maxima Duchesne × Cucurbita moschata Duchesne) rootstocks and root-knot nematode-resistant Cucumis metulifer E. Mey. ex Naud. rootstock. The grafting combinations were evaluated under different production conditions. Grafting with hybrid squash rootstocks resulted in reduced soluble solids content (SSC) and decreased sensory ratings of 'Arava' fruit. By contrast with grafted 'Arava', grafted 'Honey Yellow' did not exhibit significant differences in sensory properties and instrumental measurements regardless of production conditions and rootstock selection.

CONCLUSION

The effects of grafting on fruit quality attributes differed between the two distinctive types of melon scion used. Potential negative impacts of rootstocks on melon fruit quality need to be considered in the selection and use of disease-resistant rootstocks.

Crop load and harvest maturity effects on consumer preferences for apricots

BACKGROUND

Improving apricot fruit quality delivered to consumers is key to ensuring a successful apricot industry. Previous studies have focused on effects of either soluble solids content (SSC) or fruit firmness on consumer preferences, and results have been equivocal. This study evaluated the effects of crop load and harvest maturity how they affected on fruit SSC and firmness, and on subsequent consumer preferences.

RESULTS

SSC of apricots was an important factor only when fruit were firmer than 15 N and not immature. When fruit were softer than 15 N, SSC had little influence on consumer liking. In general, consumers preferred fruit that were grown on trees thinned to approximately 10-20% less than typical commercial crop loads and were harvested in a more mature condition. Consumers also preferred fruit that had a higher sugar/acid ratio or BrimA value, which is the °Brix - k × titratable acidity, where k is a constant that varies between species and cultivars depending on the specific acids and sugars present. High apricot flavour and juiciness were associated with greater sweetness.

CONCLUSION

Management practices that increase fruit SSC and sugar/acid ratio, such as reducing crop load, will improve consumer satisfaction as long as fruit are harvested at an adequate maturity stage and are maintained in storage so that they do not soften too quickly.

Elshof) postharvest dry matter and soluble solids content with near infrared spectroscopy

BACKGROUND

Fruit dry matter (DM) and soluble solids content (SSC) are primarily composed of carbohydrate and are standard parameters for assessing quality. Near infrared spectroscopy provides potential for non-destructive fruit quality analysis but the collinearity between DM and SSC is an issue for prediction. Shorter wavelength spectra have been used for the prediction of fruit DM and SSC, but radiation between 1000 and 2500 nm may be suitable for distinguishing between the two forms of carbohydrate.

RESULTS

Spectra and DM and SSC samples were taken for a total of 450 'Elshof' apples 30, 58 and 93 days after harvest. Regression models were built using the interval partial least squares method. Prediction models for DM and SSC for each day yielded R² values between 0.63 and 0.86 and residual predictive deviations (RPDs) between 1.7 and 2.7 for DM, and R² = 0.76-0.85 and RPDs = 2.2-2.6 for SSC.

CONCLUSION

Model RPD values were not high enough for general quantitative predictions, although they compare well to previous work. Certain factors affected model success, including changes in fruit physiology over time and the range of reference data. The complexity of absorbance spectra for DM and SSC plus their strong correlation suggests that prediction models cannot easily distinguish between soluble and non-soluble forms of carbohydrate.

Relation of biospeckle activity with quality attributes of apples

Biospeckle is nondestructive optical technique based on the analysis of variations of laser light scattered from biological samples. Biospeckle activity reflects the state of the investigated object. In this study the relation of biospeckle activity (BA) with firmness, soluble solids content (SSC), titratable acidity (TA) and starch content (SC) during the shelf life of seven apple cultivars was studied. The results showed that the quality attributes change significantly during storage. Significant and pronounced positive correlation between BA and SC was found. This result shows that degradation of starch granules, which could be stimulated to vibration by intracellular cyclosis, causes a lesser number of laser light scattering centers and results in smaller apparent biospeckle activity.