Real Food Campaign: Appendices

Appendix A

  1. Raman spectroscopy – Measures specific compounds through Raman Scattering, which is largely unique by compound. Although powerful, this option has many drawbacks. High cost and complexity are the two most difficult to design around.

    • Expensive – $17,000 for commercial handheld version from BW-Tek, many core components of the technology (the laser and the detector) have requirements that limit their ability to drop in price.
    • Bulky – Even the handheld ones are still quite large and would limit the number of people who would actually use them in the field.
    • Dangerous – Raman requires a high powered lasers which could damage the eye, and require warning labels on the device.
    • Complex – Raman produces a spectra which must be de-convoluted to use.  Current successful applications are in the pharmaceutical industry, where very specific, consistent, and pure compounds are quantified.  Pharmaceuticals are a comparatively pure material, and therefore easy to analyze, as compared to a carrot which contains thousands of compounds.
    • Minimal penetration – Raman can be done at 3 wavelengths (532nm/785nm/1064nm).  Lower wavelengths (532nm) have minimal penetration, measuring only the surface of the object.  Longer wavelengths (1064nm) will penetrate the samples but will not identify many compounds of interest.  While you can build systems with more than one wavelength, the cost and signal complexity go up.
  2. Laser-induced breakdown spectroscopy (LIBS) – Detection of specific elements of the periodic table by burning the sample using a laser and measuring the resulting spectra.  This technology is dangerous, expensive ($50,000 currently), complex to interpret, and the interpretation requires a team of trained specialists for each type of sample.  Also, this does effectively identify organic compounds.

  3. Reflection – Measure the reflection of light in the UV, visible, near infra-red, and infra-red ranges (~250 - 2000nm).  Traditional spectrometers are expensive ($500), but costs are coming down and are expected to come down further.  For example, Hamamatsu’s mini-spec is $120 and measures 350 - 850nm.  It’s possible to design reflection spectrometers which are not affected by ambient light, bringing the total manufacturing cost below $100 and improving usability. While inexpensive and easy to use, reflection has not yet been proven to relate to broad ranges of nutritional compounds, though there are reasons to believe that it likely would with enough data to correlate nutritional data with the spectral output. (See Appendix B.)

  4. Microfluidics – Also referred to as “lab on a chip” systems, the strategy is to perform chemical reactions (wet chemistry, like that used in laboratories) using tiny amounts of reagents embedded on substrate.  Classic examples are paper strip soil tests or pregnancy tests, though now diseases and nutrient deficiencies are being identified with paper-based urine tests.  There are more flexible, small scale open source technologies like DropBot and OpenDrop as well. Microfluidics can potentially directly measure specific compounds of interest, and run (in miniature) well referenced and respected lab methods.  However, unlike the aforementioned technologies microfluidics has a consumables cost, though it can be as low as $1 per test in some cases.  Additional tools, like a colorimeter, are also needed to quantify the results. Finally, the development time and cost to build the microfluidics platform and develop the methods for measuring any given compound is significant.


     

    Appendix B

    The use of UV, visible, near infrared, and infrared reflection is not new in the food industry. Reflection-based measurements have been used to determine the quality of fruits and berries. Reflection-based measurements have been proven to correlate well with the total soluble solids, titratable acidity, and flesh firmness in apples, apricots and strawberries.1,2,3 In addition to these quality parameters, reflection measurements have also been used to predict desirable traits in apples including roughness, crunchiness, mealiness, and both sweet and sour taste.4

    These traits may be desirable traits for marketing fresh foods but they do not necessarily relate to either the nutrient density or potential health benefits derived from food.

    Recently, however, reflection measurements have been correlated to various nutritional parameters important to human health. For example, reflection measurements of lettuce were correlated to the pigments anthocyanins and carotenoids, which are antioxidants, and chlorophyll, which has anti-carcinogenic properties.5

    In another example, reflection measurements were used to predict lycopene content, another phenolic compound with health benefits, found in tomatoes.6 Furthermore, reflection has been used to identify negative quality issues in fruits and vegetables such as elevated nitrate concentrations in summer squash7 and blackspot in potato.8

    In a somewhat related area, measuring reflection spectra has become a useful tool in predicting many soil parameters including organic C, pH, and soil texture. In this field, there are two approaches that have proven critical to improving the accuracy of the reflection-based predictions: 1) building up large databases of reference data9, and 2) incorporating key metadata into the models.10 When predicting soil properties, key metadata may include topography, vegetation, geological and climate data.10 In the area of food quality, it does not appear that any work has yet been done to identify metadata that may affect nutrient density.

     

    Appendix C

    Comparisons between populations in the State of Nutrition survey, and the estimated minimum statistically significant difference between the population means.  Populations are assumed to be normally distributed with a standard deviation of 1 mg/100g.  Calculations available on request in Open Calc (.ods) or Microsoft Excel (.xls) formats.

    Nationwide comparison of supermarket v farmer's market carrots

    In year 1 →


    Supermarketn = 12
    Farmers marketn = 18

    Estimated min. statistically significant mean population difference ~ 0.9 mg / 100g

    In year 3 →


    Supermarketn = 36
    Farmers marketn = 54

    Estimated min. statistically significant mean population difference ~ 0.5 mg / 100g

    Nationwide comparison of conventional, organic, and regenerative ag carrots

    In year 1 + 2 →


    Conventionaln = 24
    Organicn = 24
    Regenerative agn = 12

    Estimated min. statistically significant mean population difference ~ 0.8 mg / 100g

    In year 1 + 2 + 3 →


    Conventionaln = 36
    Organicn = 36
    Regenerative agn = 18

    Estimated min. statistically significant mean population difference ~ 0.6 mg / 100g

     

    1 Martinez Vega MV et al. J. Sci. Food Agric. 2013;93:3710–3719. http://onlinelibrary.wiley.com/doi/10.1002/jsfa.6207/abstract
    2 Amodio ML et al. Postharvest Biology and Technology. 2017;125:112–121. http://dx.doi.org/10.1016/j.postharvbio.2016.11.013
    3 Berardinelli A et al. Journal of Food Science. 2010;75(7): E462–E468. http://onlinelibrary.wiley.com/doi/10.1111/j.1750-3841.2010.01741.x/abstract
    4 Mehinagic E. et al. Food Quality and Preference. 2003;14;473–484. https://www.researchgate.net/publication/230725502_Relationship_between_sensory_analysis_penetrometry_and_visible-NIR_ spectroscopy_of_apples_belonging_to_different_cultivars
    5 Steidle Neto AJ et al. J. Sci. Food Agric. 2017;97:2015–2022. http://onlinelibrary.wiley.com/doi/10.1002/jsfa.8002/abstract
    6 Clement A et al. Quality Assurance and Safety of Crops & Foods. 2015;7(5):747–756. http://www.wageningenacademic.com/doi/abs/10.3920/QAS2014.0521
    7 Sanchez M-T et al. Postharvest Biology and Technology. 2017;125:122–128. http://dx.doi.org/10.1016/j.postharvbio.2016.11.011
    8 Lopez-Maestresalas A et al. Food Control. 2016;70:22–241. http://dx.doi.org/10.1016/j.foodcont.2016.06.001
    9 Viscarra Rossel RA et al. Earth-Science Reviews. http://dx.doi.org/10.1016/j.earscirev.2016.01.012
    10 FAO: "Prediction of soil properties with NIR data and site descriptors using preprocessing and neural networks." http://www.fao.org/fileadmin/user_upload/GSP/docs/Spectroscopy_dec13/Aitkenhead.pdf