品質檢測儀F-751是基于F-750基礎上進行開發的針對獼猴桃、芒果、牛油果和甜瓜的品質快速無損評判的便攜式儀器。它準確、無損快速測量果實的干物質量或糖度，從而評價果實的成熟度。
NIR（近紅外測定）技術在成套設備中的應用可為我們提供客觀量化的質量標準，已在生產中應用多年。我們的便攜式設備把近紅外分析技術帶給田間種植者為作物收割前提供更好、更一致的成熟度的評估和測定。F-751已經開始在世界各地的大學、科研機構和種植商使用。
主要功能：
1、精確的測量干物質量或糖度（芒果、牛油果、獼猴桃和甜瓜）
2、快速測量（4～6秒）
3、非破壞測量
4、帶定位系統，便于制作數據地圖
5、野外可視半透顯示屏
6、可更換/充電電池
7、SD卡數據存儲
8、無需創建模型
9、收獲前成熟度評估
10、采后品質檢驗
測量參數：
測量原始數據、反射率、吸光度、一階導數、二階導數、計算糖度或干物質并獲取GPS信息
應用領域：
主要應用于果實成熟度和甜度相關參數的無損評估，包括田間作物管理和收獲期評估、果實儲藏、果實催熟及果實零售的各個環節。
主要技術參數：
1、光譜儀：濱松C11708MA
2、光譜范圍：640-1050 nm
3、光譜樣點大小: 2.3nm
4、光譜分辨率：20 nm（半峰全寬）
5、光源：鹵素鎢燈
6、鏡頭：鍍膜增益近紅外線鏡頭
7、快門：白色參考標準
8、顯示器：帶背光陽光可見透反液晶屏
9、操作環境：0-50oC，0-90%（非結露）
10、數據連接：WiFi
11、記錄的數據：原始數據、反射率、吸光度、一階導數、二階導數、GPS信息、日期和時間
12、測量：干物質量&糖度（oBrix）
13、供電：可拆卸3400Ah鋰電池
14、續航時間：大于500次測量
15、數據存儲：可拆卸32GB SD卡
16、外殼：粉末噴涂鋁合金型材
17、尺寸：18×12×4.5cm
18、重量：1.05 kg
選購指南：
主機、操作手冊、葉夾 、箱子和相關配件
基本配置：
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參考文獻：
D. Valasiadis et al., Wide-characterization of high and low dry matter kiwifruit through spatiotemporal multi-omic approach. Postharvest Biology and Technology 209, 112727 (2024).
2. G. Nú?ez-Lillo et al., A First Omics Data Integration Approach in Hass Avocados to Evaluate Rootstock–Scion Interactions: From Aerial and Root Plant Growth to Fruit Development. Plants 13, 603 (2024).
3. A. Mumford, Z. Abrahamsson, I. Hale, Predicting Soluble Solids Concentration of ‘Geneva 3’ Kiwiberries Using Near Infrared Spectroscopy. HortTechnology 34, 172-180 (2024).
4. B. Giussani, G. Gorla, J. Riu, Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview. Critical Reviews in Analytical Chemistry 54, 11-43 (2024).
5. A. Zeb et al., Towards sweetness classification of orange crs using short-wave NIR spectroscopy. Scientific Reports 13, 325 (2023).
6. Y. Yu, M. Yao, Is this pear sweeter than this apple? A universal SSC model for fruits with similar physicochemical properties. Biosystems Engineering 226, 116-131 (2023).
7. M. Wohlers, A. McGlone, E. Frank, G. Holmes, Augmenting NIR Spectra in deep regression to improve calibration. Chemometrics and Intelligent Laboratory Systems 240, 104924 (2023).
8. C. B. S. Tong, M. Gullickson, M. Rogers, E. Burkness, W. D. Hutchison, Detection of Spotted-winged Drosophila (Diptera: Drosophilidae) Infestations in Blueberry Fruits1. Journal of Entomological Science 58, 370-374 (2023).
9. V. S. Titeli, M. Michailidis, G. Tanou, A. Molassiotis, Physiological and Metabolic Traits Linked to Kiwifruit Quality. Horticulturae 9, 915 (2023).
10. A. Sharma et al., Chemometrics driven portable Vis-SWNIR spectrophotometer for non-destructive quality evaluation of raw tomatoes. Chemometrics and Intelligent Laboratory Systems 242, 105001 (2023).
11. A. Praiphui, K. V. Lopin, F. Kielar, Construction and evaluation of a low cost NIR-spectrometer for the determination of mango quality parameters. Journal of Food Measurement and Characterization 17, 4125-4139 (2023).
12. A. Praiphui, F. Kielar, Comparing the performance of miniaturized near-infrared spectrometers in the evaluation of mango quality. Journal of Food Measurement and Characterization 17, 5886-5902 (2023).
13. C. Lu, H. Xu, B. Lannard, X. Yang, Seasonal Changes in Amylose and Starch Compositions in ‘Ambrosia’ Apples Associated with Rootstocks and Orchard Climatic Conditions. Agronomy 13, 2923 (2023).
14. J. E. Larson, P. Perkins-Veazie, T. M. Kon, Apple Fruitlet Abscission Prediction. II. Characteristics of Fruitlets Predicted to Persist or Abscise by Reflectance Spectroscopy Models. HortScience 58, 1095-1103 (2023).
15. J. E. Larson, T. M. Kon, Apple Fruitlet Abscission Prediction. I. Development and Evaluation of Reflectance Spectroscopy Models. HortScience 58, 1085-1092 (2023).
16. L. Duckena et al., Non-Destructive Quality Evaluation of 80 Tomato Varieties Using Vis-NIR Spectroscopy. Foods 12, 1990 (2023).
17. B. M. Anthony, D. G. Sterle, I. S. Minas, Robust non-destructive individual cr models allow for accurate peach fruit quality and maturity assessment following customization in phenotypically similar crs. Postharvest Biology and Technology 195, 112148 (2023).
產地：美國Felix
