A Handheld IoT Vis/NIR Spectroscopic System to Assess the Soluble Solids Content of Wine Grapes
The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectr...
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2025-07-01
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| author | Xu Zhang Ziquan Qin Ruijie Zhao Zhuojun Xie Xuebing Bai |
| author_facet | Xu Zhang Ziquan Qin Ruijie Zhao Zhuojun Xie Xuebing Bai |
| author_sort | Xu Zhang |
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| description | The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mi>V</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mi>V</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mi>V</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes. |
| format | Article |
| id | doaj-art-e68bc283ebf842fe9b0422fbf43a06fa |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-e68bc283ebf842fe9b0422fbf43a06fa2025-08-20T02:47:10ZengMDPI AGSensors1424-82202025-07-012514452310.3390/s25144523A Handheld IoT Vis/NIR Spectroscopic System to Assess the Soluble Solids Content of Wine GrapesXu Zhang0Ziquan Qin1Ruijie Zhao2Zhuojun Xie3Xuebing Bai4College of Enology, Northwest A&F University, Xianyang 712100, ChinaCollege of Enology, Northwest A&F University, Xianyang 712100, ChinaCollege of Enology, Northwest A&F University, Xianyang 712100, ChinaCollege of Enology, Northwest A&F University, Xianyang 712100, ChinaCollege of Enology, Northwest A&F University, Xianyang 712100, ChinaThe quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mi>V</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mi>V</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mi>V</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes.https://www.mdpi.com/1424-8220/25/14/4523wine grapevisible-near-infrared spectroscopyIoTsoluble solids contentdata optimization |
| spellingShingle | Xu Zhang Ziquan Qin Ruijie Zhao Zhuojun Xie Xuebing Bai A Handheld IoT Vis/NIR Spectroscopic System to Assess the Soluble Solids Content of Wine Grapes Sensors wine grape visible-near-infrared spectroscopy IoT soluble solids content data optimization |
| title | A Handheld IoT Vis/NIR Spectroscopic System to Assess the Soluble Solids Content of Wine Grapes |
| title_full | A Handheld IoT Vis/NIR Spectroscopic System to Assess the Soluble Solids Content of Wine Grapes |
| title_fullStr | A Handheld IoT Vis/NIR Spectroscopic System to Assess the Soluble Solids Content of Wine Grapes |
| title_full_unstemmed | A Handheld IoT Vis/NIR Spectroscopic System to Assess the Soluble Solids Content of Wine Grapes |
| title_short | A Handheld IoT Vis/NIR Spectroscopic System to Assess the Soluble Solids Content of Wine Grapes |
| title_sort | handheld iot vis nir spectroscopic system to assess the soluble solids content of wine grapes |
| topic | wine grape visible-near-infrared spectroscopy IoT soluble solids content data optimization |
| url | https://www.mdpi.com/1424-8220/25/14/4523 |
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