收藏切换
Quality Prediction of Kokotireke Grapes and Identification of Geographical Indication Products Based on Visible/Near-infrared Spectroscopy
收藏切换
Xiwei CUI1, Mingxuan LIU2, Tao JIANG2, Zhigang LIU1, *, Yan LIU1, Weiwang GUO1, Hang YU2
Science and Technology of Food Industry | 2026, 47(6) : 34 - 43
Less
收藏切换
Science and Technology of Food Industry | 2026, 47(6): 34-43
• Papers Invited by Guest Editors •
Quality Prediction of Kokotireke Grapes and Identification of Geographical Indication Products Based on Visible/Near-infrared Spectroscopy
Full
Xiwei CUI1, Mingxuan LIU2, Tao JIANG2, Zhigang LIU1, *, Yan LIU1, Weiwang GUO1, Hang YU2
Affiliations
  • 1Am Incorporation for Metrology and Testing Technology Services, Suzhou 215100, China
  • 2Jiangnan University, Wuxi 214122, China
Published: 2026-03-15 doi: 10.13386/j.issn1002-0306.2025030202
Outline
收藏切换

Kokotireke grapes, a geographical indication agricultural product in Xinjiang, exhibit the challenges in uneven quality grades and geographical indication adulteration in the market. The visible/near-infrared (Vis/NIR) spectroscopy technology was innovatively employed to perform the quality prediction of Kokotireke grapes and geographical indication identification in this study. Kokotireke grape samples with geographical indication and non-geographical indication were collected, and the spectral data, soluble solid content (SSC) and titratable acidity (TA) were measured. Subsequently, different spectral preprocessing techniques combined with the partial least squares regression (PLSR) to construct quality prediction models and partial least squares discriminant analysis (PLS-DA) geographical indication identification models. The results showed that different preprocessing methods exhibited a significant effect on the model performance, and the SG-LBC-1stD had the best preprocessing effectiveness. Specifically, the determination coefficients (R2) of PLSR model for SSC and TA prediction reached 0.9361 and 0.9478, respectively, and the root mean square errors (RMSE) were 0.3362 and 0.0368, respectively. Additionally, the PLS-DA model can effectively distinguish the products between geographical indication and non-geographical indication, achieving the identification accuracy of 0.928, R2X of 0.828, R2Y of 0.621, Q2 of 0.553, and AUC of 0.980. Overall, this research provides a rapid, online, efficient and non-destructive quality prediction and geographical indication discrimination method for the Kokotireke grape industry, which is of great significance for ensuring product quality and avoiding the geographical indication adulteration and also offers the reference for related research in other fruits.

kokotireke grapes  /  visible/near-infrared spectroscopy  /  quality prediction  /  geographical indication product identification  /  spectral preprocessing  /  chemometric modeling
崔希炜, 刘铭萱, 江涛, 刘志刚, 刘岩, 郭威望, 于航. 基于可见/近红外光谱的科克铁热克葡萄品质预测和地理标志识别. 食品工业科技, 2026 , 47 (6) : 34 -43 . DOI: 10.13386/j.issn1002-0306.2025030202
Xiwei CUI, Mingxuan LIU, Tao JIANG, Zhigang LIU, Yan LIU, Weiwang GUO, Hang YU. Quality Prediction of Kokotireke Grapes and Identification of Geographical Indication Products Based on Visible/Near-infrared Spectroscopy[J]. Science and Technology of Food Industry, 2026 , 47 (6) : 34 -43 . DOI: 10.13386/j.issn1002-0306.2025030202
Year 2026 volume 47 Issue 6
139
61
Cite this Article
BibTeX
Article Info
doi: 10.13386/j.issn1002-0306.2025030202
  • Receive Date:2025-03-17
  • Online Date:2026-10-24
  • Published:2026-03-15
Article Data
Affiliations
History
  • Received:2025-03-17
Affiliations
    1Am Incorporation for Metrology and Testing Technology Services, Suzhou 215100, China
    2Jiangnan University, Wuxi 214122, China
References
Share
https://castjournals.cast.org.cn/joweb/spgykj/EN/10.13386/j.issn1002-0306.2025030202
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT