Food Powder Classification Using a Portable Visible-Near-Infrared Spectrometer

You, Hanjong; Kim, Young‐Sik; Lee, Jae Hyung; Jang, Byung-Jun; Choi, Sunwoong

doi:10.26866/jees.2017.17.4.186

Cited by 11 publications

(10 citation statements)

References 3 publications

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“…Partial least squares regression methods provided the best predictions on the validation dataset. Gaussian process regression and RF methods require larger dataset for training [24] and did not perform as well as PLSR. Support vector machine provided reduced performance compared to PLSR, and would also be expected to improve in performance relative to PLSR with a larger dataset.…”

Section: Resultsmentioning

confidence: 99%

Predicting the quality of ryegrass using hyperspectral imaging

et al. 2019

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Background The quality of forage plants is a crucial component of animal performance and a limiting factor in pasture based production systems. Key forage attributes that may require improvement include the sugar, lipid, protein and energy contents of the vegetative parts of these plants. The aim of this study was to evaluate the potential capacity of hyperspectral imaging (HSI) for non-invasive assessment of forage chemical composition. Hyperspectral image data within the visible near-infrared range into the extended near-infrared covering 550–1700 nm wavelengths were obtained from 185 accessions of ryegrass ( Lolium perenne ), which were also analysed for 13 forage quality attributes. Results Medium to high predictive power was observed for the HSI models of total sugars (R 2 validation of 0.58), high molecular weight sugars (R 2 validation of 0.63), %Ash (R 2 validation of 0.50) and %Nitrogen (R 2 validation of 0.70). Significant HSI models with low R 2 validation of 0.1–0.5 were also obtained for low molecular weight sugars, NDF (%), ADF (%), DOMD (% DM), ME (MJ/kg DM), DM (%), Ca (mg/g) and OM (%). We also observed significant differences in the chemical composition between the pseudostems and leaves of the plants for each accession. The power of HSI for prediction of these differences within plants was also demonstrated. Conclusion This study paves the way for the HSI technology to be used for in-field estimation of forage composition attributes in perennial ryegrass. This will allow more rapid genetic-based selection and breeding for a trait that is normally expensive to measure providing a cheaper, non-destructive and high throughput screening tool.

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Section: Resultsmentioning

confidence: 99%

Predicting the quality of ryegrass using hyperspectral imaging

et al. 2019

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“…VIS-NIR spectroscopy is a new non-destructive measurement technique. Based on the different reflection and radiation information of different substances in the same spectral band, VIS-NIR spectroscopy is widely used to detect chemical substances [20,21], soil [22], minerals [23] and food [24]. The following articles in this section describe the principle of VIS-NIR spectroscopy and its application in the study of fruit tree phenotypes.…”

Section: Vis-nir Spectroscopymentioning

confidence: 99%

Phenotypic techniques and applications in fruit trees: a review

Huang

Ren

et al. 2020

Plant Methods

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Phenotypic information is of great significance for irrigation management, disease prevention and yield improvement. Interest in the evaluation of phenotypes has grown with the goal of enhancing the quality of fruit trees. Traditional techniques for monitoring fruit tree phenotypes are destructive and time-consuming. The development of advanced technology is the key to rapid and non-destructive detection. This review describes several techniques applied to fruit tree phenotypic research in the field, including visible and near-infrared (VIS-NIR) spectroscopy, digital photography, multispectral and hyperspectral imaging, thermal imaging, and light detection and ranging (LiDAR). The applications of these technologies are summarized in terms of architecture parameters, pigment and nutrient contents, water stress, biochemical parameters of fruits and disease detection. These techniques have been shown to play important roles in fruit tree phenotypic research.

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“…Performance. To investigate the effect of spectral shift on object classification using machine learning, we evaluated eight common food powders that were visually indistinguishable, including salt, sugar, cream, flour, bean, corn, rice, and potato powder [13]. The food powders were measured using a LinkSquare spectrometer [14], and the spectral data were acquired.…”

Section: The Effect Of Spectral Shift On the Classificationmentioning

confidence: 99%

“…However, the process of spectral analysis with a portable spectrometer offers certain advantages. Machine learning is employed to classify an object with a portable spectrometer [7][8][9][10][11][12][13]. The advantage of utilizing machine learning is that the classification no longer searches only for the spectral peak of an object but also considers its overall shape by analyzing properties such as slope and intensity ratio.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Device-to-Device Variation in the Spectral Response of Portable Spectrometers

Choi

Kim²,

Lee³

et al. 2019

Journal of Sensors

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As portable spectrometers have been developed, the research of spectral analysis has evolved from a traditional laboratory-based closed environment to a network-connected open environment. Consequently, its application areas are expanding in combination with machine learning techniques. The device-to-device variation in the spectral response of portable spectrometers is a critical issue in a machine learning-based service scenario since the classification performance is highly dependent on the consistency of spectral responses from each spectrometer. To minimize device-to-device variation, a cuboid prism is employed instead of a combination of mirrors and prism to construct an optical system for the spectrometer. The spectral responses are calibrated to correct pixel shift on the image sensor. Experimental results show that the proposed method can minimize the device-to-device variation in spectral response of portable spectrometers.

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Food Powder Classification Using a Portable Visible-Near-Infrared Spectrometer

Cited by 11 publications

References 3 publications

Predicting the quality of ryegrass using hyperspectral imaging

Predicting the quality of ryegrass using hyperspectral imaging

Phenotypic techniques and applications in fruit trees: a review

Minimizing Device-to-Device Variation in the Spectral Response of Portable Spectrometers

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