Miniaturized Vis–NIR handheld spectrometer for non-invasive pigment quantification in agritech applications

Dinish, U. S.; Teng, Mark Teo Ju; Xinhui, Valerie Teo; Dev, Kapil; Tan, Javier Jingheng; Koh, Sally Shuxian; Urano, Daisuke; Olivo, Malini

doi:10.1038/s41598-023-36220-2

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Several studies used spectral analysis to investigate plants in the past [12][13][14][15][16][17][18][19][20]. For example, Ge et al explored the utility of VIS-NIR-SWIR as a high-throughput instrument for measuring six leaf parameters of maize plants: chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K) [12].…”

Section: Introductionmentioning

confidence: 99%

A Portable Tool for Spectral Analysis of Plant Leaves That Incorporates a Multichannel Detector to Enable Faster Data Capture

Botero-Valencia,

Reyes-Vera,

Ospina-Rojas

et al. 2024

Instruments

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In this study, a novel system was designed to enhance the efficiency of data acquisition in a portable and compact instrument dedicated to the spectral analysis of various surfaces, including plant leaves, and materials requiring characterization within the 410 to 915 nm range. The proposed system incorporates two nine-band detectors positioned on the top and bottom of the target surface, each equipped with a digitally controllable LED. The detectors are capable of measuring both reflection and transmission properties, depending on the LED configuration. Specifically, when the upper LED is activated, the lower detector operates without its LED, enabling the precise measurement of light transmitted through the sample. The process is reversed in subsequent iterations, facilitating an accurate assessment of reflection and transmission for each side of the target surface. For reliability, the error estimation utilizes a color checker, followed by a multi-layer perceptron (MLP) implementation integrated into the microcontroller unit (MCU) using TinyML technology for real-time refined data acquisition. The system is constructed with 3D-printed components and cost-effective electronics. It also supports USB or Bluetooth communication for data transmission. This innovative detector marks a significant advancement in spectral analysis, particularly for plant research, offering the potential for disease detection and nutritional deficiency assessment.

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Section: Introductionmentioning

confidence: 99%

A Portable Tool for Spectral Analysis of Plant Leaves That Incorporates a Multichannel Detector to Enable Faster Data Capture

Botero-Valencia,

Reyes-Vera,

Ospina-Rojas

et al. 2024

Instruments

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Detection of Anthocyanins in Potatoes Using Micro-Hyperspectral Images Based on Convolutional Neural Networks

Wang,

Li,

Deng

et al. 2024

Foods

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The color potato has the function of both a food and vegetable. The color potato not only contains various amino acids and trace elements needed by the human body but also contains anthocyanins. Anthocyanins have many functions, such as antioxidation, inflammation inhibition, vision improvement, and cancer prevention, so colored potatoes are deeply loved by consumers and have good market prospects. However, at present, the detection of anthocyanin content in color potatoes mainly depends on chemical methods, which are time-consuming and laborious, so it is necessary to study a fast and accurate detection method. In this study, microscopic hyperspectral equipment was used to collect the spectral information of the outer skin and inner skin of potatoes. The original spectrum, pretreatment spectrum, and characteristic spectrum variables of the outer skin and inner skin were predicted by the convolution neural network (CNN) algorithm and partial least squares regression (PLS) algorithm, respectively, and the performance of the model was evaluated by the prediction set correlation coefficient (Rp), prediction set root mean square error (RMSEP), correction set correlation coefficient (Rc), correction set root mean square error (RMSEC), and residual prediction deviation (RPD). The results revealed that the inner skin Raw + CNN model constructed under raw spectral data is optimal with Rc = 0.9508, RMSEC = 0.0374%, Rp = 0.9461, RMSEP = 0.2361% and RPD = 4.4933. The inner skin Savitzky-Golay (SG) + Detrend (DET) + CNN model constructed from pre-processed spectral data is optimal with Rc = 0.9499, RMSEC = 0.0359%, Rp = 0.9439, RMSEP = 0.2384%, RPD = 4.6516. The inner skin DET + competitive adaptive reweighted sampling (CARS) +CNN model constructed from the feature-based spectral data was optimal with Rc = 0.9527, RMSEC = 0.0708%, Rp = 0.9457, RMSEP = 0.2711%, and RPD = 4.1623. It can be seen that the Rp, RMSEP, Rc, RMSEC, and RPD values for modeling the spectral information of the inner skin were higher than those of the outer skin under the three different spectral data. The prediction accuracy of the model built by the CNN algorithm was better than the conventional algorithm PLS, the application of the CNN algorithm in inner skin can achieve accurate prediction of anthocyanin content in potato.

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Miniaturized Vis–NIR handheld spectrometer for non-invasive pigment quantification in agritech applications

Cited by 2 publications

References 37 publications

A Portable Tool for Spectral Analysis of Plant Leaves That Incorporates a Multichannel Detector to Enable Faster Data Capture

A Portable Tool for Spectral Analysis of Plant Leaves That Incorporates a Multichannel Detector to Enable Faster Data Capture

Detection of Anthocyanins in Potatoes Using Micro-Hyperspectral Images Based on Convolutional Neural Networks

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