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Food production in conventional agriculture faces numerous challenges such as reducing waste, meeting demand, maintaining flavor, and providing nutrition. Contained environments under artificial climate control, or cyber-agriculture, could in principle be used to meet many of these challenges. Through such environments, phenotypic expression of the plant—mass, edible yield, flavor, and nutrients—can be actuated through a “climate recipe,” where light, water, nutrients, temperature, and other climate and ecological variables are optimized to achieve a desired result. This paper describes a method for doing this optimization for the desired result of flavor by combining cyber-agriculture, metabolomic phenotype (chemotype) measurements, and machine learning. In a pilot experiment, (1) environmental conditions, i.e. photoperiod and ultraviolet (UV) light (known to affect production of flavor-active molecules in edible plants) were applied under different regimes to basil plants ( Ocimum basilicum ) growing inside a hydroponic farm with an open-source design; (2) flavor-active volatile molecules were measured in each plant using gas chromatography-mass spectrometry (GC-MS); and (3) symbolic regression was used to construct a surrogate model of this chemistry from the input environmental variables, and this model was used to discover new combinations of photoperiod and UV light to increase this chemistry. These new combinations, or climate recipes, were then implemented in the hydroponic farm, and several of them resulted in a marked increase in volatiles over control. The process also led to two important insights: it demonstrated a “dilution effect”, i.e. a negative correlation between weight and desirable chemical species, and it discovered the surprising effect that a 24-hour photoperiod of photosynthetic-active radiation, the equivalent of all-day light, induces the most flavor molecule production in basil. In this manner, surrogate optimization through machine learning can be used to discover effective recipes for cyber-agriculture that would be difficult and time-consuming to find using hand-designed experiments.

This paper describes the design of a new instrumental technique, Gas Chromatography Recomposition-Olfactometry (GC-R), that adapts the reconstitution technique used in flavor chemistry studies by extracting volatiles from a sample by headspace solid-phase microextraction (SPME), separating the extract on a capillary GC column, and recombining individual compounds selectively as they elute off of the column into a mixture for sensory analysis (Figure 1). Using the chromatogram of a mixture as a map, the GC-R instrument allows the operator to “cut apart" and recombine the components of the mixture at will, selecting compounds, peaks, or sections based on retention time to include or exclude in a reconstitution for sensory analysis. Selective recombination is accomplished with the installation of a Deans Switch directly in-line with the column, which directs compounds either to waste or to a cryotrap at the operator's discretion. This enables the creation of, for example, aroma reconstitutions incorporating all of the volatiles in a sample, including instrumentally undetectable compounds as well those present at concentrations below sensory thresholds, thus correcting for the “reconstitution discrepancy" sometimes noted in flavor chemistry studies. Using only flowering lavender (Lavandula angustifola ‘Hidcote Blue’) as a source for volatiles, we used the instrument to build mixtures of subsets of lavender volatiles in-instrument and characterized their aroma qualities with a sensory panel. We showed evidence of additive, masking, and synergistic effects in these mixtures and of “lavender' aroma character as an emergent property of specific mixtures. This was accomplished without the need for chemical standards, reductive aroma models, or calculation of Odor Activity Values, and is broadly applicable to any aroma or flavor.

30Food production in conventional agriculture faces numerous challenges such as reducing 31 waste, meeting demand, maintaining flavor, and providing nutrition. Contained environments 32 under artificial climate control, or cyber-agriculture, could in principle be used to meet many of 33 these challenges. Through such environments, phenotypic expression of the plant---mass, edible 34 yield, flavor, and nutrients---can be actuated through a "climate recipe," where light, water, 35 nutrients, temperature, and other climate and ecological variables are optimized to achieve a 36 desired result. This paper describes a method for doing this optimization for the desired result of 37 flavor by combining cyber-agriculture, metabolomic phenotype (chemotype) measurements, and 38 machine learning. In a pilot experiment, (1) environmental conditions, i.e. photoperiod and 39 ultraviolet (UV) light (known to affect production of flavor-active molecules in edible plants) 40 were applied under different regimes to basil plants (Ocimum basilicum) growing inside a 41 hydroponic farm with an open-source design; (2) flavor-active volatile molecules were 42 measured in each plant using gas chromatography-mass spectrometry (GC-MS); and (3) 43 symbolic regression was used to construct a surrogate model of this chemistry from the input 44 environmental variables, and this model was used to discover new combinations of photoperiod 45 and UV light to increase this chemistry. These new combinations, or climate recipes, were then 46 implemented in the hydroponic farm, and several of them resulted in a marked increase in 47 volatiles over control. The process also led to two important insights: it demonstrated a "dilution 48 effect", i.e. a negative correlation between weight and desirable chemical species, and it 49 discovered the surprising effect that a 24-hour photoperiod of photosynthetic-active radiation, the 50 equivalent of all-day light, induces the most flavor molecule production in basil. In this manner, 51 surrogate optimization through machine learning can be used to discover effective recipes for 52 3 cyber-agriculture that would be difficult and time-consuming to find using hand-designed 53 experiments. 54 55 1 Introduction 56 The so-called "dilution effect," noted since the 1940's and systematically reviewed since 57 the early 1980's [1], describes an inverse relationship between yield and nutrient concentration in 58 food: For many nutritionally-important chemical constituents of food plants, such as minerals, 59 protein, and vitamins, an increase in biomass is accompanied by a decrease in nutrient 60 concentration. This effect has been systematically demonstrated in historical nutrient content 61 studies over the last 50-70 years [2,3], as well as in controlled side-by-side trials that have shown 62 a relationship between nutrient dilution and genetics [4], artificial fertilization [5], and elevated 63 carbon dioxide levels related to climate change [6,7]. Flavor, known to be an important element 64 of food and of ea...

Foods and beverage aroma results from multicomponent mixtures of volatile compounds present in the food that interact with olfactory receptors and produce a perceptual response in the brain. However, the perceptual interactions that occur when complex odor mixtures are combined are not well understood. Here we used Gas chromatography-Recomposition-Olfactometry (GC-R) to better understand the role that individual compounds have on the perceived sensory aroma of bitters. Bitters are the concentrated alcoholic extract of flavorful plant materials with a wide range of complex sensory and chemical aroma profiles that have not been extensively studied. Previously, we demonstrated that Angostura bitters are characterized by complex aroma attributes described as cola , ginger , orange peel , and black pepper and that the volatile composition of Angostura bitters is predominantly composed of terpenoids. Using GC-R to create in-instrument mixtures of the Angostura headspace extracts, the sensory attributes of Angostura extracts with linalool, α-terpinyl-acetate and caryophyllene omitted were evaluated. The omission experiments demonstrated direct and indirect effects of the individual compounds on the aroma attributes of Angostura bitters, through masking, additive, and synergistic interactions. Caryophyllene in particular, which was present in the headspace extracts at concentration only slightly above sensory threshold levels, had a large and unexpected impact on the sensory properties of the mixtures and may be most responsible for the aromas associated with the whole sample. The GC-R and statistical approaches used here provided valuable tools to reveal relationships among individual compounds and aroma attributes of foods that have not been currently theorized using existing analytical approaches.

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