品质检测仪F-750是一款用于分析与农产品品质密切相关的内部及外部特性的测量仪器。
NIR（近红外测定）技术在成套设备中的应用可为我们提供客观量化的质量标准，已在生产中应用多年。我们设备把近红外分析技术带给田间种植者为作物收割前提供更好、更一致的成熟度的评估和测定。
F-750使用近红外（NIR）光谱技术无损的评估品质指标，如干物质、总可溶性固体（TSS或白利糖度）。F-750具有广泛的应用从确定最佳收获时间到在包装厂和进口时对水果的品质进行客观分析。
主要功能：
1、针对农产品的品质进行检测
2、快速测量（4～6秒）
3、非破坏测量
4、全球定位系统，便于制作数据地图
5、可更换/充电电池
6、SD卡数据存储
7、可创建特殊品种的模型
8、收获前成熟度评估
9、采后质量检验
测量参数：
可测量可溶性固形物（糖度或百利糖）、干物质、内部颜色等参数
应用领域：
主要应用于果实成熟度和甜度相关参数的无损评估，包括田间作物管理和收获期评估、果实储藏、果实催熟及果实零售的各个环节。
主要技术参数：
1、光谱仪：卡尔蔡司MMS-1光谱仪
2、光谱范围：310-1100 nm
3、光谱样点大小: 3 nm
4、光谱分辨率：8-13 nm
5、光源：氙气钨灯
6、镜头：镀膜增益近红外线镜头
7、快门：白色涂漆参考标准
8、显示器：阳光可见透反液晶屏
9、数据传输：USB和WIFi
10、光谱数据输出选项：反射率，吸收率，一阶导数，二阶导数
11、操作环境：0-50ºC, 0-90% （非结露）
12、测量：吸光度、二阶导数吸光度
13、供电：可拆卸3100毫安时锂离子电池
14、续航时间：大于1600次
15、数据存储：可拆卸32GB SD卡
16、外壳：粉末喷涂铝合金型材
17、尺寸：18×12×4.4cm
18、重量：1.05 kg
选购指南：
主机、操作手册、叶夹 箱子和相关配件
基本配置：
可选附件：
用于测量小型果实，例如：蓝莓
参考文献：
1. 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. Núñez-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 cultivars 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 cultivar models allow for accurate peach fruit quality and maturity assessment following customization in phenotypically similar cultivars. Postharvest Biology and Technology 195, 112148 (2023).
产地：美国Felix
