Prediction of Soluble Solids Content and Post-Storage Internal Quality of Bulida Apricots Using near Infrared Spectroscopy

The development of internal breakdown of South African Bulida apricots during cold storage, rendering the fruit unsuitable for canning, causes significant post-harvest losses. Regression models to predict internal post-storage quality using near infrared (NIR) spectroscopy and multivariate classification techniques were developed using NIR spectra of the intact fruit collected prior to storage and subjective quality evaluations performed after a cold storage period of four weeks. A correct classification rate of 69% was obtained using multivariate adaptive regression splines (MARS) compared to 50% obtained by soft independent modelling by class analogy (SIMCA). NIR regression models developed for soluble solids content (SSC) of intact fruit as well as for direct NIR measurements on the exposed fruit tissue gave similar results, thus confirming sufficient NIR light penetration into the intact fruit. The best prediction results were obtained when two spectral measurements per fruit (one on each half of the fruit), compared to single measurements, were used.

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Online quantitative analysis of soluble solids content in navel oranges using visible-near infrared spectroscopy and variable selection methods

Journal of Innovative Optical Health Sciences ◽

10.1142/s179354581350065x ◽

2014 ◽

Vol 07 (06) ◽

pp. 1350065 ◽

Cited By ~ 6

Author(s):

Yande Liu ◽

Yanrui Zhou ◽

Yuanyuan Pan

Keyword(s):

Quantitative Analysis ◽

Variable Selection ◽

Near Infrared ◽

Nir Spectroscopy ◽

Soluble Solids ◽

Internal Quality ◽

Soluble Solids Content ◽

Selection Methods ◽

Calibration Models ◽

Solids Content

Variable selection is applied widely for visible-near infrared (Vis-NIR) spectroscopy analysis of internal quality in fruits. Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content (SSC) in navel oranges. Moving window partial least squares (MW-PLS), Monte Carlo uninformative variables elimination (MC-UVE) and wavelet transform (WT) combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges. The performances of these methods were compared for modeling the Vis-NIR data sets of navel orange samples. Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation coefficient (r) of 0.89 and lower root mean square error of prediction (RMSEP) of 0.54 at 5 fruits per second. It concluded that Vis-NIR spectroscopy coupled with WT-MC-UVE may be a fast and effective tool for online quantitative analysis of SSC in navel oranges.

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ASSESSMENT OF APPLE FRUITS UNDER COLD STORAGE

FRUIT GROWING RESEARCH ◽

10.33045/fgr.v37.2021.20 ◽

2021 ◽

Vol 37 (37) ◽

pp. 141-146

Author(s):

Eugenia Maresi ◽

◽

Madalina Militaru ◽

Madalina Butac ◽

Adelina Zoican ◽

...

Keyword(s):

Cold Storage ◽

Storage Period ◽

Soluble Solids ◽

Soluble Solids Content ◽

Chemical Parameters ◽

Before And After ◽

Fruit Growing ◽

Solids Content ◽

Physical And Chemical ◽

Apple Fruits

The cold storage of fruits for a long period of time without quantitative and qualitative depreciations is absolutely necessary to supply the market with fresh fruits. This study aimed to investigate the changes in apple fruits during cold storage in order to determine their optimal storage capacity and to know the optimal moment of market sale. Five apple cvs. (‘Rumina’, ‘Rebra’, ‘Rustic’, ‘Generos’ and ‘Florina’) grown in the Genetics and Breeding Department of Research Institute for Fruit Growing Pitesti, Romania were kept in the cold storage at 2-4ºC and 90-95% humidity for 4 months. Before and after storage in cold conditions, the following physical and chemical parameters of fruits were evaluated: weight, color, firmness, soluble solids content and acids content. After 4 months, the fruits weight decreased with 2.45 g. The lowest weight loss was recorded on ‘Rumina’ cv. (1.48 g) and the highest for ‘Rebra’ cv. (3.20 g). At the end of storage period (January) the fruits firmness decreases with 7.74 Bareiss HPE-II FFF units, the best results being recorded on Rumina cv. (loss of firmness by only 5.23 units). Also, after cold storage the taste of the fruits was improved (the soluble solids content increased with 0.29 % Brix and the acid content decreased with 0.22 g/100 g fresh weight). The fruits color has changed gradually during the storage, the fruits being more colorful and attractive.

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NONDESTRUCTIVE TESTING OF SOLUBLE SOLIDS CONTENT IN CERASUS HUMILIS USING VISIBLE / NEAR-INFRARED SPECTROSCOPY COUPLED WITH WAVELENGTH SELECTION ALGORITHM

INMATEH Agricultural Engineering ◽

10.35633/inmateh-61-28 ◽

2020 ◽

Vol 61 (2) ◽

pp. 251-262 ◽

Cited By ~ 2

Author(s):

Bin Wang ◽

Junlin He ◽

Shujuan Zhang ◽

Lili Li

Keyword(s):

Infrared Spectroscopy ◽

Nondestructive Testing ◽

Linear Model ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Soluble Solids ◽

Internal Quality ◽

Soluble Solids Content ◽

Cerasus Humilis ◽

Solids Content

Soluble solids content (SSC) is one of the most important quality attributes affecting the taste and maturity of fresh fruit. In this study, with the cerasus humilis fruit as the research object, a prediction model of soluble solid content (SSC) in cerasus humilis (CH) is established based on visible / near-infrared spectroscopy to explore a nondestructive testing method of the interior quality of CH. The visible / near-infrared spectral info (350-2500nm) of 160 CHs was collected to extract the reflection spectrum, establishing the linear model (PLSR) and non-linear model (LS-SVM) of CH’s spectral info and SSC. The prediction performance and stability of the model were justified using several statistical indicators namely correlation coefficient of the prediction set (Rp), the root mean square error of the prediction set (RMSEP), and the residual predictive deviation (RPD) index. Results showed that multiplicative scatter correction (MSC) was proved to be the best preprocessing method, UVE-CARS was the optimal method of dimension reduction, the quantities of characteristic wavelengths was 10 and the optimal model was UVE-CARS-PLSR, in which Rc is 0.8995, Rp is 0.8579, RMSEC is 0.8897, RMSEP is 0.9059, and RPD is 1.8766, indicating that the redundant data of the original spectrum can be reduced, the wavelength dimensions can be reduced, valid info can be retained and data processing can be simplified as UVE-CARS extracts characteristic wavelengths. Reference and theoretical basis are provided in this research for future research and development of portable detector and online sorting detection of CH internal quality.

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THE DEVELOPMENT OF PORTABLE DETECTOR FOR APPLE' S SOLUBLE SOLIDS CONTENT BASED ON VISIBLE AND NEAR INFRARED SPECTRUM

INMATEH Agricultural Engineering ◽

10.35633/inmateh-62-29 ◽

2020 ◽

pp. 277-288

Author(s):

Fa Peng ◽

ShuangXi Liu ◽

Hao Jiang ◽

XueMei Liu ◽

JunLin Mu ◽

...

Keyword(s):

Infrared Spectrum ◽

Prediction Model ◽

Spectral Data ◽

Near Infrared ◽

Mobile App ◽

Partial Least Square ◽

Soluble Solids ◽

Soluble Solids Content ◽

Solids Content ◽

Near Infrared Spectrum

In order to detect the soluble solids content of apples quickly and accurately, a portable apple soluble solids content detector based on USB2000 + micro spectrometer was developed. The instrument can communicate with computer terminal and mobile app through network port, Bluetooth and other ways, which can realize the rapid acquisition of apple spectral information. Firstly, the visible / near-infrared spectrum data and soluble solids content information of 160 apple samples were collected; secondly, the spectral data preprocessing methods were compared, and the results showed that the prediction model of sugar content based on partial least square (PLS) method after average smoothing preprocessing was accurate. The correlation coefficient (RP) and root mean square error (RMSEP) of the prediction model were 0.902 and 0.589 ° Brix, respectively. Finally, on the basis of average smoothing preprocessing, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the wavelength of spectral data, and PLS model was constructed based on the selected 17 characteristic wavelengths, which can increase the accuracy of soluble solids content prediction model, increase the RP to 0.912, and reduce RMSEP to 0.511 ° Brix. The portable visible / near infrared spectrum soluble solids prediction model based on the instrument and method has high accuracy, and the detector can quickly and accurately measure the soluble solids content of apple.

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