The quality detector F-751 is a portable instrument developed based on the F-750 for the rapid and lossless evaluation of the quality of kiwi fruit, mango, avocado and melon.It accurately and non-destructively quickly measure the dry mass or sugar level of the fruit, thereby evaluating the ripening degree of the fruit.

The application of NIR (near infrared measurement) technology in complete sets of equipment can provide us with objective quantitative quality standards and has been used in production for many years.Our portable devices bring near-infrared analysis technology to field growers to provide better and more consistent maturity assessment and determination before crop harvesting.The F-751 has begun to be used in universities, research institutions and growers around the world.

Main functions:

1. Accurately measure dry matter quality or sugar content (mango, avocado, kiwi and melon)

2. Quick measurement (4~6 seconds)

3. Non-destructive measurement

4. With a global positioning system, it is easy to make data maps

5. Wild visual semi-transparent display

6. Replaceable/rechargeable battery

7. SD card data storage

8. No need to create a model

9. Pre-harvest maturity assessment

10. Post-harvest quality inspection

Measurement parameters:

Measure raw data, reflectivity, absorbance, first-order derivative, second-order derivative, calculate sugar or dry matter and obtain GPS information

Application areas:

It is mainly used for non-destructive assessment of fruit ripening and sweetness related parameters, including field crop management and harvesting period assessment, fruit storage, fruit ripening and fruit retail.

Main technical parameters:

1. Spectrometer: Hamamatsu C11708MA

2. Spectral range: 640-1050 nm

3. Spectral sample point size: 2.3nm

4. Spectral resolution: maximum 20 nm (full width of half peak)

5. Light source: halogen tungsten lamp

6. Lens: Coated gain near-infrared lens

7. Shutter: White reference standard

8. Monitor: Backlight sunlight visible and transparent LCD screen

9. Operating environment: 0-50ºC, 0-90% (non-condensing)

10. Data connection: WiFi

11. Recorded data: original data, reflectivity, absorbance, first-order derivative, second-order derivative, GPS information, date and time

12. Measurement: dry matter mass & sugar content (ºBrix)

13. Power supply: removable 3400Ah lithium battery

14. Battery life: greater than 500 measurements

15. Data storage: removable 32GB SD card

16. Case: Powder spray aluminum alloy profile

17. Size: 18×12×4.5cm

18. Weight: 1.05 kg

Purchase Guide:

Host, operating manual, leaf clip, box and related accessories

Basic configuration:

References:

D. val Asia DISETA., wide-characterization of high and low dry matter kiwifruit through spa TiO temporal multi-O meters approach.post harvest biology and technology209, 112727 (2024).

2.G. Núñez-LilloETA., A first OM ICS data integration approach in ha SS avocados to evaluate rootstock–scion interactions: from aerial and root plant growth to fruit development.plants13, 603 (2024).

3.A. Mumford, Z. Abraham is son, i. Hale, predicting solution solids concentration of ‘Geneva 3’ kiwi berries using near infrared spectroscopy.ho RT technology34, 172-180 (2024).

4.B. GI US 3 i, G. go hot, J. RI U, analytical chemistry strategies int and US EOF mini Atu rise DN IR instruments: an overview.critical review sin analytical chemistry54, 11-43 (2024).

5.A. Z EBETA., towards sweetness classification of orange culturers using short-wave NIR spectroscopy.scientific reports13, 325 (2023).

6.Y. Y U, M. YO, is this pearl sweeter than this apple? A universals SC model for fruits with similar Phys ICO chemical properties.biosystems engineering226, 116-131 (2023).

7.M. w oh RS, A. MC GL one, E. Frank, G. Holmes, augment ing NIR spectra in EP regression to improve calibration.c and MO metrics and intelligent laboratories systems240, 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 enomological science58, 370-374 (2023).

9.V. S. TI special case, M. MI split case third, G. tan, A. mol A dead Otis, physical and metabolic traits linked to kiwifruit quality.ho RT ICU L Suddenly AE9, 915 (2023).

10.A. SHA peopleETA., c and MO metrics drive portable vis-SW NI R spectrophotometer for non-destructive quality evaluation of raw tomatoes.c and MO metrics and intelligent laboratories systems242, 105001 (2023).

11.A. PR AIP Club, K. V. Lo products, F. Kiel AR, construction and evaluation of low cost NIR-spectrometer fort and determination of mango quality parameters.journal of food measurement and characterization17, 4125-4139 (2023).

12.A. PR AIP, F. Kiel AR, comparing the performance of miniaturized near-infrared spectrometers int and evaluation of mango quality.journal of food measurement and characterization17, 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.Agronomy13, 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.ho RT science58, 1095-1103 (2023).

15.J. E. Larson, T. M. KO you, apple fruitlet abscission prediction. i. development and evaluation of reflection spectroscopy models.ho RT science58, 1085-1092 (2023).

16.l. duck EETA., non-destructive quality evaluation of 80 tomato varieties using vis-n IR spectroscopy.foods12, 1990 (2023).

17.B. M. Anthony, D. G. Sterle, I. S. Minas, Robust non-destructive individual cultural models allow for accurate peach fruit quality and maturity assessment following customization in phenotypically similar culturers.post harvest biology and technology195, 112148 (2023).

Origin: Felix, USA