品質(zhì)檢測(cè)儀F-750是一款用于分析與農(nóng)產(chǎn)品品質(zhì)密切相關(guān)的內(nèi)部及外部特性的測(cè)量?jī)x器。

       NIR（近紅外測(cè)定）技術(shù)在成套設(shè)備中的應(yīng)用可為我們提供客觀量化的質(zhì)量標(biāo)準(zhǔn)，已在生產(chǎn)中應(yīng)用多年。我們?cè)O(shè)備把近紅外分析技術(shù)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(píng)估和測(cè)定。

       F-750使用近紅外（NIR）光譜技術(shù)無(wú)損的評(píng)估品質(zhì)指標(biāo)，如干物質(zhì)、總可溶性固體（TSS或白利糖度）。F-750具有廣泛的應(yīng)用從確定最佳收獲時(shí)間到在包裝廠(chǎng)和進(jìn)口時(shí)對(duì)水果的品質(zhì)進(jìn)行客觀分析。

主要功能：

1、針對(duì)農(nóng)產(chǎn)品的品質(zhì)進(jìn)行檢測(cè)

2、快速測(cè)量（4～6秒）

3、非破壞測(cè)量

4、全球定位系統(tǒng)，便于制作數(shù)據(jù)地圖

5、可更換/充電電池

6、SD卡數(shù)據(jù)存儲(chǔ)

7、可創(chuàng)建特殊品種的模型

8、收獲前成熟度評(píng)估

9、采后質(zhì)量檢驗(yàn)

測(cè)量參數(shù)：

可測(cè)量可溶性固形物（糖度或百利糖）、干物質(zhì)、內(nèi)部顏色等參數(shù)

應(yīng)用領(lǐng)域：

主要應(yīng)用于果實(shí)成熟度和甜度相關(guān)參數(shù)的無(wú)損評(píng)估，包括田間作物管理和收獲期評(píng)估、果實(shí)儲(chǔ)藏、果實(shí)催熟及果實(shí)零售的各個(gè)環(huán)節(jié)。

主要技術(shù)參數(shù)：

1、光譜儀：卡爾蔡司MMS-1光譜儀

2、光譜范圍：310-1100 nm

3、光譜樣點(diǎn)大小: 3 nm

4、光譜分辨率：8-13 nm

5、光源：氙氣鎢燈

6、鏡頭：鍍膜增益近紅外線(xiàn)鏡頭

7、快門(mén)：白色涂漆參考標(biāo)準(zhǔn)

8、顯示器：陽(yáng)光可見(jiàn)透反液晶屏

9、數(shù)據(jù)傳輸：USB和WIFi

10、光譜數(shù)據(jù)輸出選項(xiàng)：反射率，吸收率，一階導(dǎo)數(shù)，二階導(dǎo)數(shù)

11、操作環(huán)境：0-50oC, 0-90% （非結(jié)露）

12、測(cè)量：吸光度、二階導(dǎo)數(shù)吸光度

13、供電：可拆卸3100毫安時(shí)鋰離子電池

14、續(xù)航時(shí)間：大于1600次

15、數(shù)據(jù)存儲(chǔ)：可拆卸32GB SD卡

16、外殼：粉末噴涂鋁合金型材

17、尺寸：18×12×4.4cm

18、重量：1.05 kg

選購(gòu)指南：

主機(jī)、操作手冊(cè)、葉夾 箱子和相關(guān)配件

基本配置：

可選附件：

用于測(cè)量小型果實(shí)，例如：藍(lán)莓

參考文獻(xiàn)：

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. 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 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).

產(chǎn)地：美國(guó)Felix