The Near-IR spectra that are used for quality control of food are quite complex to analyze. They contain overtones and combination spectra which are broad-band and a few orders of magnitude lower in intensity as compared to the Mid-IR intensity of the fundamental band1,2. Here is an example of Near-IR spectra.
shows a typical Near-IR spectrum.
Figure 1: A typical Near-IR spectrum with combination bands and overtones.
Many suppliers of food and nutritional products are interested in a handheld near-IR instrument that can measure the ingredients of their raw supplies and also sort them. This is not only a hardware challenge but measuring the reflectance spectrum by the hardware and converting it to the absorption spectrum by the app is only the first step of this process. The most difficult part is to use a chemometrics model such as a Principal Component Analysis (PCA) for sorting/identification or a Partial Least Squares (PLS) model for quantitative predictions. Each model should have its own set of calibration spectra which should preferably be accessible through a cloud platform. Lastly an app is needed which could have a two way communication with the cloud. The measurement taken by the instrument is directly saved on the cloud and the latter, in turn, runs the specific model and sends back the results of evaluation back to the user. This way, for example an unskilled worker could perform all the measurements by the press of a button and classify or quantify the objects under test rapidly and accurately.
Allied Scientific Pro (ASP) offers
the Nirvascan spectrometer which is a handheld spectro-photometer that measures
the reflectance spectrum of an object in the range 900-1700 nm. Figure 2 shows
the reflective model measuring from wheat kernels in order to determine their
protein and moisture contents.
Figure 2: Nirvascan instrument measuring from wheat kernels
Although developing a cloud based app is a near future goal, ASP has laid the foundation by developing a PC-run cloud called NirvaCloud. It is possible for a user to upload spectra and make a classification of a group of spectra under a NIR Profile. One can plot the NIR profiles containing hundreds of spectra of say wine grapes all at once. Figure 3 shows the plotting of a grape NIR profile.
Figure 3: NIR profile of grapes
One can plot the NIR profiles
containing hundreds of spectra of say wine grapes all at once. Figure 3 shows
the plotting of a grape NIR profile. One can make a PCA model or a PLS model
from different sets of NIR profiles and apply them to a test set. As an example
the following PCA model was made using the NIR profiles of grape, ground durum,
tomato and garlic as shown in figure 3.
Figure 5: Cloud matching using Nirva-Cloud
Nirva-Cloud is also capable of doing
quantitative predictions by creating PLS models from a calibration data set and
applying it against a validation set. It is important to note that the
pre-processing of data will improve the R-squared of prediction because it gets
rid of the offsets and makes absorption peaks more visible. Figure 6 shows a
NIR data set from grapes and its corresponding pre-processed data after
application of the second derivative and a Savitzky-Golay filter for removal of
noise. The pre-processing of data can be done directly by Nirva-Cloud.
Figure 6: Comparison of the raw spectra of grapes with its pre-processed spectra
The PLS model made out of the
pre-processed grape spectra was applied to the pre-processed grape validation
set and the following predictions were obtained as shown in figure 7.
Figure 7: R-Squared of 0.87 is obtained for the PLS prediction after pre-processing the spectra
If the pre-processing had not been done, the R-Squared would have been nearly 0.5.
The cloud service is available to customers for a 3 months free trial and after that subscription fees will be charged. Customers also have the option of contributing to the cloud’s library by sharing the data and possibly collecting royalty charges.
1- Dahm DJ, Dahm KD. 2001. The Physics of Near-Infrared Scattering. In Williams P, Norris K, editors. Near Infrared Technology in the Agricultural and Food Industries, 2nd ed., Saint Paul: American Association of Cereal Chemists, p 19-37.
2- C.E. Miller, “Chemical Principles of Near Infrared Technology”, Chapter 2 in Near Infrared Technology: In the Agricultural and Food Industry, P. Williams and K. Norris (Editors), Amer. Assn. of Cereal Chemists; 2nd Ed. (November 15, 2001) .