Prediction and Classification of Energy Content in Growing Cane Stalks for Breeding Programmes Using Visible and Shortwave Near Infrared

Phuphaphud, Arthit; Saengprachatanarug, Khwantri; Posom, Jetsada; Taira, Eizo; Panduangnate, Lalita

doi:10.1007/s12355-021-01075-2

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…The statistical values obtained from the experimental data were within an acceptable range, suggesting that the mathematical models used to quantify the sucrose levels in this investigation were satisfactory and suitable for potential implementation in agriculture [28]. In other studies, R 2 values of 0.81 were obtained for sucrose in potato tuber stored at 12°C temperature [29], R 2 values of 0.78 were obtained for sucrose in mandarin fruit [30], and R 2 values of 0.92 were obtained for sucrose in molasses stored at 25°C [31].…”

Section: Plsr Modelsupporting

confidence: 63%

Partial Least Square Regression for Nondestructive Determination of Sucrose Content of Healthy and Fusarium spp. Infected Potato (Solanum tuberosum L.) Utilizing Visible and Near-Infrared Spectroscopy

Prasetyo,

Amanah,

Farras

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Conventional methods of quantifying the chemical content of potatoes at different storage temperatures are time-consuming and expensive. This research studied the Visible and Near Infrared (Vis-NIR) spectroscopy for possible rapid and nondestructive methods. In this study, healthy and Fusarium spp. Potato seeds of Granola L varieties were infected artificially through the process of inoculation of fungi, and healthy potatoes were stored in various post-harvest storage conditions, namely temperatures 12°C, 25°C and a combination of temperatures 12°C and 25°C. VIS-NIR spectral data from seeds are observed periodically during the storage period. The study results showed that Vis-NIR predicted sucrose content in potatoes. The best-developed PLSR calibration model for potatoes stored at 25°C and a combination of 12°C and 25°C show R2c of 0.87 and 0.83 and RMSEC of 0.26 and 0.28. The models also successfully predicted the sugar content of potato stored at 25°C and a combination of temperatures 12°C and 25°C with R2p 0.75 and 0.78, RMSEP of 0.36 and 0.32, and RPD of 1.99 and 2.81 for sucrose. The developed model of sucrose content or potato storage temperatures of 12°C is not recommended for monitoring and detection due to the low RPD < 1.9 even though the R2c values are 0.65 – 0.9. the results of this investigation indicate that VIS-NIR spectroscopy could potentially serve as a tool for quantifying the chemical composition of potatoes during post-harvest storage.

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Section: Plsr Modelsupporting

confidence: 63%

Partial Least Square Regression for Nondestructive Determination of Sucrose Content of Healthy and Fusarium spp. Infected Potato (Solanum tuberosum L.) Utilizing Visible and Near-Infrared Spectroscopy

Prasetyo,

Amanah,

Farras

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…For instance, Posom et al [14] developed a reliable online method for measuring the HHV of sugarcane using NIRS. Phuphaphud et al [15] developed spectroscopic models using visible and shortwave NIR to predict and classify the energy content of growing cane stalks for breeding programs. Huang et al [10] developed a prediction model for the HHV as well as the elemental composition (C, H, and N) of straw using NIRS.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Assessment of Biomass Properties for Energy Usage Using Near-Infrared Spectroscopy and Spectral Multi-Preprocessing Techniques

et al. 2023

Self Cite

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In this study, partial least squares regression (PLSR) models were developed using no preprocessing, traditional preprocessing, multi-preprocessing 5-range, multi-preprocessing 3-range, a genetic algorithm (GA), and a successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage by employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis were compared, and the best model was selected based on the coefficient of determination of the validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on the model performance results, the proposed HHV model from GA-PLSR and the N models from the multi-preprocessing PLSR 5-range could be used for most applications, including research, whereas the C and H models from GA-PLSR and the O model from the multi-preprocessing PLSR 5 range method 5-range air performance and are applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving these models by 4.1839%, 8.1842%, 3.7587%, and 4.0085%, respectively. Therefore, this method can be considered a reliable and non-destructive alternative method for rapidly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for robust application.

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“…All these reference patches were then experimentally characterized using the proposed low-cost spectrometer. Figure 8 shows only six of them for clarity (patch 01, patch 04, patch 10, patch 14, patch 15, and patch 19). A comparison of the reference reflectance (MEA), raw reflectance (SEN), and adjusted reflectance (ADJ) is shown in each scenario.…”

Section: Model Selection and Training Errormentioning

confidence: 99%

“…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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Prediction and Classification of Energy Content in Growing Cane Stalks for Breeding Programmes Using Visible and Shortwave Near Infrared

Cited by 7 publications

References 42 publications

Partial Least Square Regression for Nondestructive Determination of Sucrose Content of Healthy and Fusarium spp. Infected Potato (Solanum tuberosum L.) Utilizing Visible and Near-Infrared Spectroscopy

Partial Least Square Regression for Nondestructive Determination of Sucrose Content of Healthy and Fusarium spp. Infected Potato (Solanum tuberosum L.) Utilizing Visible and Near-Infrared Spectroscopy

Comprehensive Assessment of Biomass Properties for Energy Usage Using Near-Infrared Spectroscopy and Spectral Multi-Preprocessing Techniques

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

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