Detection of the Soluble Solid Contents from Fresh Jujubes during Different Maturation Periods Using NIR Hyperspectral Imaging and an Artificial Bee Colony

Sun, Haixia; Zhang, Shujuan; Chen, Caihong; Li, Chengji; Xing, Shuhai; Liu, Jianglong; Xue, Jianxin

doi:10.1155/2019/5032950

Cited by 17 publications

(7 citation statements)

References 32 publications

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“…All results from the analysis of the samples are presented in Table 1. Water-soluble dry matter is an important parameter in the quality assessment of fruit, juice, and concentrates because of affecting the taste (especially sweetness) of the fruit and it directly affects the willingness to buy the product [36,37]. The highest water-soluble dry matter (Brix%) content obtained from the analysis of the samples was 76.70% in the S12 coded sample, and the lowest 70% was in the N18 coded sample.…”

Section: Physicochemical Analysis Resultsmentioning

confidence: 99%

Determination of physical, chemical and antioxidant properties of pomegranate sauces sold in Turkish markets

KAMIŞ

Akar

BALTACI

2022

Turkish Journal of Analytical Chemistry

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In this study, it was aimed to determine the physical, chemical and antioxidant properties of pomegranate sauces sold in the market in Turkey. A total of 18 pomegranate sauces were studied; 17 of which were purchased from the market, and one of them was produced in a rotary evaporator in the laboratory. For each sample, analysis of antioxidant activity, titratable acidity, brix, hydroxymethyl furfural, pH, color analysis, and sugar analyzes were performed. Antioxidant activity values were analyzed using 6 different methods (DPPH (2,2-diphenyl-1-picrylhydrazil) radical scavenging activity, ferric reducing antioxidant power (FRAP), ABTS●+ radical scavenging capacity, total antioxidant capacity assay (TAC), the total phenolic content (TPC) and total flavonoid content (TFC) methods). The lowest and highest values were determined for methods of DPPH (5.23- 2822.69 mg AA/kg), FRAP (57.94 - 2380.94 mg FeSO4/kg), TAC (660.47 - 3690.83 mg AA/kg), TFC (23.06 - 11680.71 mg QEE/kg), TPC (123.54 -9566.95 mg GAE/kg). Brix, pH and % titration acidity values varied between 70.00 - 76.70%, 1.66 -2.88, 2.65 -7.58%, respectively. In addition, HMF values were measured below the limit values in most of the samples, while these values were found to be high in some of the samples. The highest and lowest HMF values were measured between 4.58 -103.68 mg/kg, respectively. Many factors such as raw materials, additives, applied heat treatments, production processes and storage conditions are effective on produced pomegranate sauces. If the production conditions are regulated in accordance with the standards, the HMF values in pomegranate sauce will be below the limit.

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Section: Physicochemical Analysis Resultsmentioning

confidence: 99%

Determination of physical, chemical and antioxidant properties of pomegranate sauces sold in Turkish markets

KAMIŞ

Akar

BALTACI

2022

Turkish Journal of Analytical Chemistry

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“…For the performance evaluation of the model, the determination coefficient of the calibration set ( R C 2 ), the determination coefficient of prediction ( R p 2 ), the root mean square error of calibration set (RMSEC), the root mean square error of prediction (RMSEP), and the residual predictive deviation (RPD) were used as the evaluation indicator (Sun et al, 2019). The RPD is 1.4–2 for a reliable model while RPD < 1.4 for an unreliable one (Y. Liu et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…), the root mean square error of calibration set (RMSEC), the root mean square error of prediction (RMSEP), and the residual predictive deviation (RPD) were used as the evaluation indicator (Sun et al, 2019). The RPD is 1.4-2 for a reliable model while RPD < 1.4 for an unreliable one (Y.…”

Section: Evaluation Indexmentioning

confidence: 99%

Mass detection of walnut based on X‐ray imaging technology

Gao

Zhang

Sun

et al. 2022

J Food Process Engineering

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In view of the problem of low accuracy in walnut mass detection caused by relatively inconstant density, this study suggested the integration of X‐ray imaging technology and image processing technology with machine learning for walnut mass detection. Using image processing technology to remove the background of walnut X‐ray image and to segment the kernels, the mass prediction models could be constructed after extracting the shape and texture of walnut characteristic parameters and the kernel shape characteristic parameters. The results revealed that when mass prediction models were built based on walnut shape characteristic parameters or constructed with the application of texture characteristic parameters, the determination coefficient (R2) were rather low, even the residual predictive deviation (RPD) less than 1.4, proving that these models were not reliable. However, when mass prediction models were built based on walnut and kernel shape characteristic parameters, the R2 for both partial least squares (PLS) and radial basis function (RBF) models were higher than 0.84. And the RPD values were 1.8133 and 1.7474, respectively. On this basis, when competitive adaptive reweighed sampling (CARS) optimized parameters were adopted, the R2 for both PLS and RBF models were higher than 0.86 and the RPD were 1.8759 and 1.8850, respectively. The result proved that the application of walnut and kernel shape characteristics can improve the accuracy of model prediction. Therefore, using X‐ray imaging technology to detect walnut mass was feasible, which could realized the prompt, accurate and nondestructive detection of walnut mass. This technology provided a novel thought for the accurate grading of walnuts. Practical Applications At present, there is a problem of similar size but large mass difference after walnut size classification, which directly affects the commodity value of walnuts. It is necessary to combine multiple characteristics to select and classify walnuts. Mass is an important grading index. With the development of machine vision technology, online detection of fruit mass has been realized. However, due to the relatively inconstant density of walnuts and the limitation of the technology, the machine vision technique cannot obtain internal information. This study used X‐ray transmission to obtain the internal information of walnuts without destroying the walnuts. At the same time, the mass detection models were established by combining the internal and external characteristics of the walnuts to achieve fast, accurate, and nondestructive testing of walnut mass. Automatic mass detection provides technical support for intelligent grading, which has important production significance and economic value for the development of the walnut industry.

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“…The damage [ 16 ], pest [ 17 ], crack [ 18 ], SSC [ 19 ], and hardness [ 20 ] have been carried out in the quality detection of fresh jujubes using VIS/NIR spectroscopy. Those quality detections were implemented in the open-field cultivation mode, and the predicted samples had similar characteristics to those modeling samples.…”

Section: Introductionmentioning

confidence: 99%

Detection of Soluble Solids Content in Different Cultivated Fresh Jujubes Based on Variable Optimization and Model Update

Sun

Zhang

Ren

et al. 2022

Foods

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To solve the failure problem of the visible/near infrared (VIS/NIR) spectroscopy model, soluble solids content (SSC) detection for fresh jujubes cultivated in different modes was carried out based on the method of variable optimization and model update. Iteratively retained informative variables (IRIV) and successive projections algorithm (SPA) algorithms were used to extract characteristic wavelengths, and least square support vector machine (LS-SVM) was used to establish detection models. Compared with IRIV, IRIV-SPA achieved better performance. Combined with the offset properties of the wavelength, repeated wavelengths were removed, and wavelength recombination was carried out to create a new combination of variables. Using these fused wavelengths, the model was recalibrated based on the Euclidean distance between samples. The LS-SVM detection model of SSC was established using the update method of wavelength fusion-Euclidean distance. Good prediction results were achieved using the proposed model. The determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the test set on SSC of fresh jujubes cultivated in the open field were 0.82, 1.49%, and 2.18, respectively. The R2, RMSE, and RPD of the test set on SSC of fresh jujubes cultivated in the rain shelter were 0.81, 1.44%, and 2.17, respectively. This study realized the SSC detection of fresh jujubes with different cultivation and provided a method for the establishment of a robust VIS/NIR detection model for fruit quality, effectively addressing the industry need for identifying jujubes grown in the open field.

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Detection of the Soluble Solid Contents from Fresh Jujubes during Different Maturation Periods Using NIR Hyperspectral Imaging and an Artificial Bee Colony

Cited by 17 publications

References 32 publications

Determination of physical, chemical and antioxidant properties of pomegranate sauces sold in Turkish markets

Determination of physical, chemical and antioxidant properties of pomegranate sauces sold in Turkish markets

Mass detection of walnut based on X‐ray imaging technology

Detection of Soluble Solids Content in Different Cultivated Fresh Jujubes Based on Variable Optimization and Model Update

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