Measurement of moisture, soluble solids, sucrose content and mechanical properties in sugar beet using portable visible and near-infrared spectroscopy

Pan, Leiqing; Lu, Renfu; Zhu, Qibing; McGrath, J. Mitchell; Tu, Kang

doi:10.1016/j.postharvbio.2015.02.005

Cited by 81 publications

(45 citation statements)

References 35 publications

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“…Near infrared spectroscopy (NIRS) has been applied as rapid and low-cost technique to determine the quality of various foods and agricultural crops (Chen et al 2015;Mata Sánchez et al 2015;Pan et al 2015;Shao et al 2015). Additionally, it has the advantage of a minimum sample preparation required before measurements (González-Martín et al 2011;Chen et al 2012a, b).…”

Section: Introductionmentioning

confidence: 99%

Rapid Determination of Antioxidant Compounds and Antioxidant Activity of Sudanese Karkade (Hibiscus sabdariffa L.) Using Near Infrared Spectroscopy

et al. 2015

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The extraction conditions applied in destructive analysis of Hibiscus sabdariffa L. (Hs) matrices before the measurement of antioxidant compounds may reduce their activities during isolation and purification due to decomposition. Hence, near infrared spectroscopic system was applied to determine these compounds. The calibration models developed by using partial least squares regression (PLSR) with 48 Hs samples and 24 unknown samples were used to confirm the robustness of the developed model. The results of calibration models effectively allowed the determination of the concentrations of total phenol content (TPC), total flavonoid content (TFC), total antioxidant content (TAC), ferric reducing antioxidant power (FRAP), and 1,1-diphenyl-2-picrylhydrazyl (DPPH) with high correlation coefficients (0.85-0.91) and small standard error of cross-validation (RMSECV) ranged from 0.08 to 0.33. The prediction capacity (RPD) for TPC, TFC, TAC, FRAP, and DPPH ranged from 3.30 to 8.25, thus demonstrating that the near infrared spectroscopic (NIRS) equations developed were applicable to unknown samples. This study demonstrates that NIRS can be considered as a fast tool for the nondestructive determination of antioxidant compounds (phenolics and flavonoids) and antioxidant activity of the Hs.

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Section: Introductionmentioning

confidence: 99%

Rapid Determination of Antioxidant Compounds and Antioxidant Activity of Sudanese Karkade (Hibiscus sabdariffa L.) Using Near Infrared Spectroscopy

et al. 2015

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“…They found that the root strength increases with storage time compared to the results obtained in the penetration test. Pan et al (2015) determined the three mechanical parameters of axially compressed sugar beet samples. These were: the maximal damaging force of a 23% strain, the area below the force-displacement curve and the inclination of the force-displacement curve.…”

Section: Introductionmentioning

confidence: 99%

Impact characteristics of sugar beet root during postharvest storage

2019

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The effects of time of sugar beet root storage on the parameters determined by impact testing were studied. The 9 mm diameter and 20 mm high cylindrical samples were cut crosswise and lengthwise along the root axis. The impact velocity was 1 m s-1 and the change in the response force over time was observed at both ends of the sample. The measurements were made directly after harvesting and after 24, 48, 96 and 120 h of storage at room temperature. Failure stress and strain, impact energy, absorbed energy, Young's modulus and shock wave speed were determined in the sample tissue from the change in the response force at both ends of the sample. A drop of 40% in the average values of the energy required to damage the samples was observed during 120 h of storage. A similar dependence was found for the energy absorbed by the sample as evidenced by the greater susceptibility of the roots to impact loads. The experiment confirms the importance of critical stress criterion for cylindrical samples of sugar beet. The velocities of shock wave propagation obtained from the samples during impact were in the range of 220-384 m s-1 and were not correlated with the other experimental parameters. K e y w o r d s: sugar beet root, impact energy, absorbed energy, storage time

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“…The near infrared spectroscopy is very sensitive to hydrocarbons, hydroxyls, and amines among the organic matters, and correspondingly, information such as proteins, starch, moisture, and fat etc. that the sample contains can be reflected [24] [6]. In recent years, high-spectrum technology makes success in the field of food quality inspection [7], and it is also adopted in the task of seed detection and sorting.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging Technology and Transfer Learning Utilized in Haploid Maize Seeds Identification

Liao

Wang

et al. 2019

2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)

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It is extremely important to correctly identify the cultivars of maize seeds in the breeding process of maize. In this paper, the transfer learning as a method of deep learning is adopted to establish a model by combining with the hyperspectral imaging technology. The haploid seeds can be recognized from large amount of diploid maize ones with great accuracy through the model. First, the information of maize seeds on each wave band is collected using the hyperspectral imaging technology, and then the recognition model is built on VGG-19 network, which is pre-trained by large-scale computer vision database (Image-Net). The correct identification rate of model utilizing seed spectral images containing 256 wave bands (862.5-1704.2nm) reaches 96.32%, and the correct identification rate of the model utilizing the seed spectral images containing single-band reaches 95.75%. The experimental results show that, CNN model which is pre-trained by visible light image database can be applied to the near-infrared hyperspectral imaging-based identification of maize seeds, and high accurate identification rate can be achieved. Meanwhile, when there is small amount of data samples, it can still realize high recognition by using transfer learning. The model not only meets the requirements of breeding recognition, but also greatly reduce the cost occurred in sample collection.

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Measurement of moisture, soluble solids, sucrose content and mechanical properties in sugar beet using portable visible and near-infrared spectroscopy

Cited by 81 publications

References 35 publications

Rapid Determination of Antioxidant Compounds and Antioxidant Activity of Sudanese Karkade (Hibiscus sabdariffa L.) Using Near Infrared Spectroscopy

Rapid Determination of Antioxidant Compounds and Antioxidant Activity of Sudanese Karkade (Hibiscus sabdariffa L.) Using Near Infrared Spectroscopy

Impact characteristics of sugar beet root during postharvest storage

Hyperspectral Imaging Technology and Transfer Learning Utilized in Haploid Maize Seeds Identification

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