Food and feed laboratories share several similarities when facing the implementation of liquid-chromatographic analysis. Using the experience acquired over the years, through application chemistry in food and feed research, selected analytes of relevance for both areas were discussed. This review focused on the common obstacles and peculiarities that each analyte offers (during the sample treatment or the chromatographic separation) throughout the implementation of said methods. A brief description of the techniques which we considered to be more pertinent, commonly used to assay such analytes is provided, including approaches using commonly available detectors (especially in starter labs) as well as mass detection. This manuscript consists of three sections: feed analysis (as the start of the food chain); food destined for human consumption determinations (the end of the food chain); and finally, assays shared by either matrices or laboratories. Analytes discussed consist of both those considered undesirable substances, contaminants, additives, and those related to nutritional quality. Our review is comprised of the examination of polyphenols, capsaicinoids, theobromine and caffeine, cholesterol, mycotoxins, antibiotics, amino acids, triphenylmethane dyes, nitrates/nitrites, ethanol soluble carbohydrates/sugars, organic acids, carotenoids, hydro and liposoluble vitamins. All analytes are currently assayed in our laboratories.

Avocado (a fruit that represents a billion-dollar industry) has become a relevant crop in global trade. The benefits of eating avocados have also been thoroughly described as they contain important nutrients needed to ensure biological functions. For example, avocados contain considerable amounts of vitamins and other phytonutrients, such as carotenoids (e.g., β-carotene), which are fat-soluble. Hence, there is a need to assess accurately these types of compounds. Herein we describe a method that chromatographically separates commercial standard solutions containing both fat-soluble vitamins (vitamin A acetate and palmitate, Vitamin D2 and D3, vitamin K1, α-, δ-, and γ-vitamin E isomers) and carotenoids (β-cryptoxanthin, zeaxanthin, lutein, β-carotene, and lycopene) effectively (i.e., analytical recoveries ranging from 80.43% to 117.02%, for vitamins, and from 43.80% to 108.63%). We optimized saponification conditions and settled at 80 °C using 1 mmol KOH L−1 ethanol during 1 h. We used a non-aqueous gradient that included methanol and methyl tert-butyl ether (starting at an 80:20 ratio) and a C30 chromatographic column to achieve analyte separation (in less than 40 min) and applied this method to avocado, a fruit that characteristically contains both types of compounds. We obtained a method with good linearity at the mid to low range of the mg L−1 (determination coefficients 0.9006–0.9964). To determine both types of compounds in avocado, we developed and validated for the simultaneous analysis of carotenoids and fat-soluble vitamins based on liquid chromatography and single quadrupole mass detection (LC/MS). From actual avocado samples, we found relevant concentrations for cholecalciferol (ranging from 103.5 to 119.5), δ-tocopherol (ranging from 6.16 to 42.48), and lutein (ranging from 6.41 to 15.13 mg/100 g dry weight basis). Simmonds cultivar demonstrated the higher values for all analytes (ranging from 0.03 (zeaxanthin) to 119.5 (cholecalciferol) mg/100 g dry weight basis).

Starchy ingredients are a key source of carbohydrates and have an essential role in a healthy diet. Starch amount in foodstuffs is paramount as it allows diet professionals to base their formulations on scientific data. Herein, the total (TS) and resistant starch (RS) content, in a selection of typical starchy foods available on the Costa Rican market, for both human and animal consumption, is reported. The major types of starch, including physically encapsulated starch, were determined using in vitro methods AOAC OMA SM methods 996.11, 2014.10, 996.11, 2002.02 and AACC 76–13.01 and 32–40.01. Samples were collected during 5 years as part of national surveillance plans. For feedstuffs, n = 252 feed ingredients (e.g., cornmeal and wheat products), n = 103 feeds (e.g., dairy and beef cattle), and n = 150 feed ingredient samples (selected based on their usage in feed formulations) were assessed for RS. In food commodities, sample numbers ascended to n = 287 and n = 371 for TS and RS, respectively (e.g. bananas). Feed ingredients with higher TS values were cassava meal, bakery by-products, rice/broken, sweet potato, and cornmeal (93.37, 81.67, 72.33, 66.66, and 61.43 g/100 g, respectively). TS for beef and dairy cattle, pig, and calf feeds, ranged from 30.26 to 34.46 g/100 g. Plantain/green banana flour, as a feed ingredient, exhibited RS absolute and relative contributions of 37.04 g/100 g and 53.89%, respectively. Products with a higher TS content included banana flour, green plantain flour, japonica rice, and cassava flour (62.87, 63.10, 72.90, 83.37 g/100 g). The primary RS sources in the Costa Rican diet are, in absolute terms, green plantain and malanga (50.41 and 56.59 g/100 g). Depending on a person's food habits, these sources may contribute in the range of 20–30 grams of RS per day. TS and RS intake may vary considerably among ingredients, and the contribution of RS may be of nutritional importance for specific individuals.

This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique’s principle and focused on said technologies’ applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.

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Method validation within food science is a not only paramount to assess method certainty and ensure the quality of the results, but a pennant in analytical chemistry. Proximate analysis is an indispensable requirement for food characterization. To improve proximate analysis, automated protein and thermogravimetric methods were validated according to international guidelines (including ISO 17025) and acceptance criteria of results based on certified reference materials and participation within international recognized proficiency schemes. Common food groups (e.g., meat, dairy, and grain products) were included and at the end of validation, we obtained three rugged and accurate methods with adequate z scores (−2 ≥ x ≤ 2) and recoveries (92–105%). During optimization, variables such as gas flows, subsample masses, and temperatures were varied and specific conditions (those that rendered the best results) were selected for each food group. For each validated method, a comparison (technical and economic) among the data obtained and the data extracted for its traditional counterpart were included: assays validated demonstrate to be more cost-effective labor-wise (ca. 9 and 16-fold) than their traditional alternatives. Specifically for combustion assay regression analysis ( y = 0.9361 x , y = 1.1001 x , and y = 0.9739 x , for meat, dairy and grain products, respectively) were performed to assess the factor, if any, which must be applied to the results to effectively match those obtained for Kjeldahl method. Finally, in the case of protein, samples can be analyzed under 5 min with no residue and a subsample mass below 400 mg. Moisture and ash analysis can be performed simultaneously using the same subsample. Data herein will also help harmonize and advance food analysis toward more efficient greener methods for proximate analysis.

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