scholarly journals On-The-Go VIS + SW − NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard

Molecules ◽  
2019 ◽  
Vol 24 (15) ◽  
pp. 2795 ◽  
Author(s):  
Fernández-Novales ◽  
Tardáguila ◽  
Gutiérrez ◽  
Paz Diago
Keyword(s):  
Cross Validation ◽  
Prediction Models ◽  
External Validation ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Spectral Measurements ◽  
Total Polyphenols ◽  
Total Soluble Solids ◽  
Grape Quality ◽  
Reliable Monitoring

Visible-Short Wave Near Infrared (VIS + SW − NIR) spectroscopy is a real alternative to break down the next barrier in precision viticulture allowing a reliable monitoring of grape composition within the vineyard to facilitate the decision-making process dealing with grape quality sorting and harvest scheduling, for example. On-the-go spectral measurements of grape clusters were acquired in the field using a VIS + SW − NIR spectrometer, operating in the 570–990 nm spectral range, from a motorized platform moving at 5 km/h. Spectral measurements were acquired along four dates during grape ripening in 2017 on the east side of the canopy, which had been partially defoliated at cluster closure. Over the whole measuring season, a total of 144 experimental blocks were monitored, sampled and their fruit analyzed for total soluble solids (TSS), anthocyanin and total polyphenols concentrations using standard, wet chemistry reference methods. Partial Least Squares (PLS) regression was used as the algorithm for training the grape composition parameters’ prediction models. The best cross-validation and external validation (prediction) models yielded determination coefficients of cross-validation (R2cv) and prediction (R2P) of 0.92 and 0.95 for TSS, R2cv = 0.75, and R2p = 0.79 for anthocyanins, and R2cv = 0.42 and R2p = 0.43 for total polyphenols. The vineyard variability maps generated for the different dates using this technology illustrate the capability to monitor the spatiotemporal dynamics and distribution of total soluble solids, anthocyanins and total polyphenols along grape ripening in a commercial vineyard.

scholarly journals Non-invasive Monitoring of Berry Ripening Using On-the-Go Hyperspectral Imaging in the Vineyard

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2534
Author(s):  
Juan Fernández-Novales ◽  
Ignacio Barrio ◽  
María Paz Diago
Keyword(s):  
Hyperspectral Imaging ◽  
Malic Acid ◽  
Prediction Models ◽  
External Validation ◽  
Titratable Acidity ◽  
Soluble Solids ◽  
Total Polyphenols ◽  
Grape Quality ◽  
Entire Experiment ◽  
La Rioja

Hyperspectral imaging offers enormous potential for measuring grape composition with a high degree of representativity, allowing all exposed grapes from the cluster to be examined non-destructively. On-the-go hyperspectral images were acquired using a push broom hyperspectral camera (400–100 nm) that was mounted in the front part of a motorized platform moving at 5 km/h in a commercial Tempranillo vineyard in La Rioja, Spain. Measurements were collected on three dates during grape ripening in 2018 on the east side of the canopy, which was defoliated in the basal fruiting zone. A total of 144 grape clusters were measured for Total soluble solids (TSS), Titratable acidity (TA), pH, Tartaric and Malic acid, Anthocyanins and Total polyphenols, using standard wet chemistry reference methods, throughout the entire experiment. Partial Least Squares (PLS) regression was used to build calibration, cross validation and prediction models for the grape composition parameters. The best performances returned determination coefficients values of external validation (R2p) of 0.82 for TSS, 0.81 for Titratable acidity, 0.61 for pH, 0.62 for Tartaric acid, 0.84 for Malic acid, 0.88 for Anthocyanins and 0.55 for Total polyphenols. The promising results exposed in this work disclosed a notable methodology on-the-go for the non-destructive, in-field assessment of grape quality composition parameters along the ripening period.

Limitations to the measurement of intact melon total soluble solids using near infrared spectroscopy

2006 ◽  
Vol 57 (4) ◽  
pp. 403 ◽  
Author(s):  
Robert L. Long ◽  
Kerry B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Quality Standard ◽  
Sampling Procedure ◽  
Performance Model ◽  
Soluble Solids ◽  
Fruit Storage ◽  
Total Soluble Solids ◽  
Fruit Maturity

The imposition of a minimum total soluble solids (TSS) value as a quality standard for orange-flesh netted melon fruit (Cucumis melo L. reticulatus group) requires either a batch sampling procedure (i.e. the estimation of the mean and standard deviation of a population), or the individual assessment of fruit [e.g. using a non-destructive procedure such as near infrared (NIR) spectroscopy]. Several potential limitations to the NIR assessment of fruit, including the variation in TSS within fruit and the effect of fruit storage conditions on the robustness of calibration models, were considered in this study. Outer mesocarp TSS was 3 TSS units higher at the stylar end of the fruit compared with the stem end, and the TSS of inner mesocarp was higher than outer tissue and more uniform across spatial positions. The linear relationship between the outer 10 mm and the subsequent middle 10 mm of tissue varied with fruit maturity [e.g. 42 days before harvest (DBH), r 2 = 0.8; 13 DBH, r 2 = 0.4; 0 DBH, r 2 = 0.7], and with cultivars (at fruit maturity, Eastern Star 2001 r 2 = 0.88; Malibu 2001 r 2 = 0.59). This relationship notably affected NIR calibration performance (e.g. based on inner mesocarp TSS; R c 2 = 0.80, root mean standard error of cross-validation (RMSECV) = 0.65, and R c 2 = 0.41, RMSECV = 0.88 for mature Eastern Star and Malibu fruit, respectively). Cold storage of fruit (0–14 days at 5°C) did not affect NIR model performance. Model performance was equivalent when based on either that part of the fruit in contact with the ground or equatorial positions; however, it was improved when based on the stylar end of the fruit.

Measurement of total soluble solids and moisture in puree and dry powder of Kakadu plum (Terminalia ferdinanadiana) samples using hand-held near infrared spectroscopy

2021 ◽  
pp. 096703352098236
Author(s):  
Eshetu M Bobasa ◽  
Michael E Netzel ◽  
Daniel Cozzolino ◽  
Anh Dao Thi Phan ◽  
Yasmina Sultanbawa
Keyword(s):  
Value Chain ◽  
Near Infrared ◽  
Cross Validation ◽  
Soluble Solids ◽  
Dry Powder ◽  
Aboriginal Communities ◽  
Total Soluble Solids ◽  
Fruit Samples ◽  
Kakadu Plum ◽  
Fruit Purée

Recent research has shown the potential of portable and handheld NIR instruments to monitor and measure the composition of fruits and vegetables. Current research has also shown the possibility of using portable instruments as tools to monitor composition along the entire food value chain. The objective of this study was to evaluate two sample presentation methods (dry powder and fruit puree) to measure total soluble solids (TSS) and moisture (M) in wild harvested Kakadu plum (KP) ( Terminalia ferdinandiana, Combretaceae). Kakadu plum is an endemic plant of Australia that contains high concentrations of vitamin C, ellagic acid as well as other bioactive compounds. These properties make this plant of high economic and social importance for the Aboriginal communities of Australia. Fruit samples were wild harvested in January 2020 from locations in the Kimberley region (Western Australia, Australia) and analysed using both reference and NIR spectroscopic methods. The SECV and RPD values in cross validation were 0.65% (RPD: 2.2) and 0.22% (RPD: 4.2) to predict M and TSS in the KP dry powder samples. The SECV and RPD values obtained in cross validation for the KP fruit puree samples were 0.56% (RPD: 2.8) and 0.24% (RPD: 3.8) for M and TSS, respectively. The results of this study demonstrated the ability of NIR spectroscopy to measure M and TSS in wild harvest fruit. These findings can be also utilised by the Aboriginal communities to develop a grading/sorting system to rapidly screen and evaluate relevant chemical parameters associated with fruit quality and safety.

scholarly journals Bruise Damage and Quality Changes in Impact-Bruised, Stored Tomatoes

Horticulturae ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. 113
Author(s):  
Pankaj B. Pathare ◽  
Mai Al-Dairi
Keyword(s):  
Weight Loss ◽  
Prediction Models ◽  
Storage Temperature ◽  
Color Change ◽  
Quality Attributes ◽  
Soluble Solids ◽  
Drop Height ◽  
Total Soluble Solids ◽  
The Impact ◽  
And Storage

This study examined three main possible effects (impact, storage temperature, and duration) that cause and extend the level of bruising and other quality attributes contributing to the deterioration of tomatoes. The impact threshold level required to cause bruising was conducted by subjecting tomato samples to a steel ball with a known mass from different drop heights (20, 40, and 60 cm). The samples were then divided and stored at 10 and 22 °C for 10 days for the further analysis of bruise area and any physiological, chemical, and nutritional changes at two day intervals. Six prediction models were constructed for the bruised area and other quality attribute changes of the tomato. Storage time, bruise area, weight loss, redness, total color change, color index, total soluble solids, and pigments content (lycopene and carotenoids) showed a significant (p < 0.05) increase with the increase of drop height (impact level) and storage temperature. After 10 days of storage, high drop impact and storage at 22 °C generated a higher reduction in firmness, lightness, yellowness, and hue° (color purity). Additionally, regression model findings showed the significant effect of storage duration, storage temperature, and drop height on the measured variables (bruise area, weight loss, firmness, redness, total soluble solids, and lycopene) at a 5% probability level with a determination coefficient (R2) ranging from 0.76 to 0.95. Bruising and other quality attributes could be reduced by reducing the temperature during storage. This study can help tomato transporters, handlers, and suppliers to understand the mechanism of bruising occurrence and how to reduce it.

scholarly journals The overall precision test for near infrared scanning and reference method for the determination of soluble solids content and pH of mango for processing factory

2020 ◽  
Vol 187 ◽  
pp. 04003
Author(s):  
Nphatsanan Saksangium ◽  
Panmanas Sirisomboon
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Reference Test ◽  
Infrared Scanning ◽  
Solids Content ◽  
Precision Test

Near infrared (NIR) spectroscopy is a rapid technique for nondestructive testing. Mango is popular fruit in Thailand. Therefore, The main aim of this paper is to report an overall precision of the NIR spectroscopy instruments and reference methods for determination at the beginning of the experiment for prediction models development to be in the mango applied processing factory.. Results showed that the repeatability of FT-NIR spectrometer and UV-VIS-NIR spectrometer were 0.00191 and 0.00529, respectively. The reproducibility of FT-NIR spectrometer and UV-VIS-NIR spectrometer were 0.00323 and 0.03561, respectively. Repeatability of reference test of TSS and pH were 0.1657 and 0.0827. Therefore, the R2max of TSS and pH were 0.9825 and 0.9504 which indicates that it is possible to develop NIR model for prediction of total soluble solids and pH.

scholarly journals Application of a Handheld Near-Infrared Spectrometer to Predict Gelatinized Starch, Fiber Fractions, and Mineral Content of Ground and Intact Extruded Dry Dog Food

Animals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1660
Author(s):  
Arianna Goi ◽  
Marica Simoni ◽  
Federico Righi ◽  
Giulio Visentin ◽  
Massimo De Marchi
Keyword(s):  
Mineral Content ◽  
Near Infrared ◽  
Cross Validation ◽  
Prediction Models ◽  
External Validation ◽  
Dog Food ◽  
Gelatinized Starch ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer ◽  
Total Starch

The aim of the present study was to investigate the ability of a handheld near-infrared spectrometer to predict total and gelatinized starch, insoluble fibrous fractions, and mineral content in extruded dry dog food. Intact and ground samples were compared to determine if the homogenization could improve the prediction performance of the instrument. Reference analyses were performed on 81 samples for starch and 99 for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and minerals, and reflectance infrared spectra (740 to 1070 nm) were recorded with a SCiO™ near-infrared (NIR) spectrometer. Prediction models were developed using modified partial least squares regression and both internal (leave-one-out cross-validation) and external validation. The best prediction models in cross-validation using ground samples were obtained for gelatinized starch (residual predictive deviation, RPD = 2.54) and total starch (RPD = 2.33), and S (RPD = 1.92), while the best using intact samples were obtained for gelatinized starch (RPD = 2.45), total starch (RPD = 2.08), and K (RPD = 1.98). Through external validation, the best statistics were obtained for gelatinized starch, with an RPD of 2.55 and 2.03 in ground and intact samples, respectively. Overall, there was no difference in prediction models accuracy using ground or intact samples. In conclusion, the miniaturized NIR instrument offers the potential for screening purposes only for total and gelatinized starch, S, and K, whereas the results do not support its applicability for the other traits.

scholarly journals Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 148 ◽  
Author(s):  
Irwin R. Donis-González ◽  
Constantino Valero ◽  
Md Abdul Momin ◽  
Amanjot Kaur ◽  
David C. Slaughter
Keyword(s):  
Prediction Models ◽  
Quality Attributes ◽  
Table Grape ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Soluble Solids ◽  
Grape Quality ◽  
Portable Spectrometers ◽  
Spectrometer Data

Near-infrared (NIR) spectroscopy has been used to non-destructively and rapidly evaluate the quality of fresh agricultural produce. In this study, two commercially available portable spectrometers (F-750: Felix Instruments, WA, USA; and SCiO: Consumer Physics, Tel Aviv, Israel) were evaluated in the wavelength range between 740 and 1070 nm to non-invasively predict quality attributes, including the dry matter (DM), and total soluble solids (TSS) content of three fresh table grape cultivars (‘Autumn Royal’, ‘Timpson’, and ‘Sweet Scarlet’) and one peach cultivar (‘Cassie’). Prediction models were developed using partial least-square regression (PLSR) to correlate the NIR absorbance spectra with the invasive quality measurements. In regard to grapes, the best DM prediction models yielded an R2 of 0.83 and 0.81, a ratio of standard error of performance to standard deviation (RPD) of 2.35 and 2.29, and a root mean square error of prediction (RMSEP) of 1.40 and 1.44; and the best TSS prediction models generated an R2 of 0.97 and 0.95, an RPD of 5.95 and 4.48, and an RMSEP of 0.53 and 0.70 for the F-750 and SCiO spectrometers, respectively. Overall, PLSR prediction models using both spectrometers were promising to predict table grape quality attributes. Regarding peach, the PLSR prediction models did not perform as well as in grapes, as DM prediction models resulted in an R2 of 0.81 and 0.67, an RPD of 2.24 and 1.74, and an RMSEP of 1.28 and 1.66; and TSS resulted in an R2 of 0.62 and 0.55, an RPD of 1.55 and 1.48, and an RMSEP of 1.19 and 1.25 for the F-750 and SCiO spectrometers, respectively. Overall, the F-750 spectrometer prediction models performed better than those generated by using the SCiO spectrometer data.

scholarly journals Could Ovarian Cancer Prediction Models Improve the Triage of Symptomatic Women in Primary Care? A Modelling Study Using Routinely Collected Data

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2886
Author(s):  
Garth Funston ◽  
Gary Abel ◽  
Emma J. Crosbie ◽  
Willie Hamilton ◽  
Fiona M. Walter
Keyword(s):  
Ovarian Cancer ◽  
Primary Care ◽  
Cross Validation ◽  
Prediction Models ◽  
Probability Model ◽  
External Validation ◽  
Economic Implications ◽  
Cancer Registry Data ◽  
Lower Specificity ◽  
Level Model

CA125 is widely used as an initial investigation in women presenting with symptoms of possible ovarian cancer. We sought to develop CA125-based diagnostic prediction models and to explore potential implications of implementing model-based thresholds for further investigation in primary care. This retrospective cohort study used routinely collected primary care and cancer registry data from symptomatic, CA125-tested women in England (2011–2014). A total of 29,962 women were included, of whom 279 were diagnosed with ovarian cancer. Logistic regression was used to develop two models to estimate ovarian cancer probability: Model 1 consisted of age and CA125 level; Model 2 incorporated further risk factors. Model discrimination (AUC) was evaluated using 10-fold cross-validation. The sensitivity and specificity of various model risk thresholds (≥1% to ≥3%) were compared with that of the current CA125 cut-off (≥35 U/mL). Model 1 exhibited excellent discrimination (AUC: 0.94) on cross-validation. The inclusion of additional variables (Model 2) did not improve performance. At a risk threshold of ≥1%, Model 1 exhibited greater sensitivity (86.4% vs. 78.5%) but lower specificity (89.1% vs. 94.5%) than CA125 (≥35 U/mL). Applying the ≥1% model threshold to the cohort in place of the current CA125 cut-off, 1 in every 74 additional women identified had ovarian cancer. Following external validation, Model 1 could be used as part of a ‘risk-based triage’ system in which women at high risk of undiagnosed ovarian cancer are selected for urgent specialist investigation, while women at ‘low risk but not no risk’ are offered non-urgent investigation or interval CA125 re-testing. Such an approach has the potential to expedite ovarian cancer diagnosis, but further research is needed to evaluate the clinical impact and health–economic implications.

Assessment of amino acids and total soluble solids in intact grape berries using contactless Vis and NIR spectroscopy during ripening

Talanta ◽  
2019 ◽  
Vol 199 ◽  
pp. 244-253 ◽  
Author(s):  
Juan Fernández-Novales ◽  
Teresa Garde-Cerdán ◽  
Javier Tardáguila ◽  
Gastón Gutiérrez-Gamboa ◽  
Eva Pilar Pérez-Álvarez ◽  
...  
Keyword(s):  
Amino Acids ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Total Soluble Solids ◽  
Grape Berries

scholarly journals Discrimination of Sweet Cherry Cultivars Based on Electronic Tongue Potentiometric Fingerprints

2020 ◽  
Vol 10 (20) ◽  
pp. 7053
Author(s):  
Isabel Rodrigues ◽  
Nuno Rodrigues ◽  
Ítala M. G. Marx ◽  
Ana C. A. Veloso ◽  
Ana Cristina Ramos ◽  
...  
Keyword(s):  
Cross Validation ◽  
Sweet Cherry ◽  
Simulated Annealing Algorithm ◽  
Electronic Tongue ◽  
Soluble Solids ◽  
Total Soluble Solids ◽  
Discriminant Model ◽  
Penetration Force ◽  
Sweet Cherry Cultivars ◽  
Fold Cross Validation

Sweet cherry is highly appreciated by its characteristic flavor, which conditions the consumer’s preference. In this study, four sweet cherry cultivars (Durona, Lapins, Summit, and Van cultivars) were characterized according to biometric (fruit and stone weights, length, maximum and minimum diameters, pulp/stone mass ratio), physicochemical (CIELAB color, penetration force, titratable acidity, and total soluble solids), and potentiometric profiles (recorded by a lab-made electronic tongue with lipid polymeric membranes). Biometric and physicochemical data were significantly cultivar-dependent (p-value < 0.0001, one-way ANOVA). Summit cherries had higher masses and dimensions. Lapins cherries had the highest penetration force values having, together with Summit cherries, the highest CIELAB values. Van cherries showed the highest total soluble solids contents. No significant differences were found for fruits’ acidity (similar titratable acidities). The possibility of discriminating cherry cultivars was also evaluated using a linear discriminant analysis/simulated-annealing algorithm. A discriminant model was established based on nine non-redundant biometric-physicochemical parameters (using a low-level data fusion), with low sensitivity (75 ± 15% for the repeated K-fold cross-validation). On the contrary, a discriminant model, based on the potentiometric fingerprints of 11 selected sensors, allowed a better discrimination, with sensitivities of 88 ± 7% for the repeated K-fold cross-validation procedure. Thus, the electronic tongue could be used as a practical tool to discriminate cherry cultivars and, if applied by fruit traders, may reduce the risk of mislabeling, increasing the consumers’ confidence when purchasing this high-value product.

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