FELIX   F-750

F-750   
9,357.50

The F-750 Produce Quality Meter is a portable measurement tool that analyzes both the internal and external traits that contribute to produce quality. NIR (Near Infrared analysis) has been used in fixed installations to provide Objective Quantifiable Quality standards in produce sorting for years. Our portable battery operated device allows a grower to take NIR analysis into the field where better and more consistent maturity determination can add value to a crop before harvest. aBuilding a Model The F-750

The F-750 reports quantitative estimates of substances (e.g. Chlorophyll), traits that involve multiple substances (e.g. ripeness, TSS, DM) and qualitative metrics (e.g. flavor index, personal preference index).

Non-destructive estimation of fruit maturity and sweetness related parameters:

• Non-destructive estimation of fruit maturity and sweetness related parameters:
  • Total Soluble Solids (Brix)
  • Dry Matter
  • Internal Color
  • External Color
  • Titratable Acid
• GPS for easy crop mapping
• Transflective display for outdoor viewing
• Rechargeable/replaceable batteries
• SD card data storage

• 10 – 200 fruit subjects are measured using the F-750 Produce Quality Meter
• The quality parameter of interest is measured on each of the subjects using an alternative destructive method (ie; Brix is measured using a refractometer).
• The included model building software combines the data from step 1 and step 2 to create a new model.
• The F-750 can now use the newly created model to non-destructively estimate the parameter of interest.

Unlike traditional spectroscopy which uses the ratio of spectral bands, F-750 measurements are calculated using a PLS based model built from a training set using user or software selected spectra from 310-1100 nm.

The model building software uses non-linear iterative partial least squares regression (NIPLS) to create coefficients that weight each wavelength based on their relationship to the known values. The F-750 calculates the second derivative spectra for the specimen and applies the training set coefficients to each wavelength to deliver the actual measurement figure.

The difference between the specimen spectra and training set spectra along with the ratio of the actual influence vs. the intended weight of each coefficient, are combined to deliver an accurate confidence score with each measurement.