Food quality is a dominating factor influencing public health and affecting people, irrespective of age, race, gender, and income, and has raised concerns worldwide. It is known to all that food quality is influenced by many factors, such as climate change, harvesting, processing, drying, and packaging. To be specific, climate change, with increasing temperatures and atmospheric carbon dioxide levels, results in yield losses and the reduction of protein and essential micronutrients (Agrimonti et al., 2021). The contents of primary and secondary metabolites in food were related to harvest time significantly (Akhatou & Fernández-Recamales, 2014). Appropriate processing including washing (e.g., water or adding chemicals in water) (Subramaniam et al., 2021) and drying (e.g., high-temperature-based, dielectric-based, low-temperature, radiation, and hybrid drying techniques) (Fathi et al., 2022) can reduce postharvest losses of nutrients. Intelligent packaging, such as informative and corrective responsive packaging, could continuously monitor food status, enable food safety and quality remediation, and convey to users (Zhang et al., 2021). Ultimately, the nutrients in food that people eat every day have the most direct influence on human health. There are countless nutritional constituents existing in food, including fats, amino acids, proteins, flavonoids, carbohydrates, vitamins, and minerals. Many of them have been identified as functional, such as flavonoids (Xiao, 2017). However, the information is not conveyed to consumers properly through the nutrition facts labels using current quality control (QC) methods. To be specific, the recommended daily intake of nutrients was updated by the US Food & Drug Administration (2022) as follows: 275 g total carbohydrate, 78 g fat, 50 g protein, 50 g added sugars, 28 g dietary fiber, 20 g saturated fat, microelements, and vitamins. Of the list, the contents of total fat (saturated and trans fat), cholesterol, sodium, total carbohydrate, and protein, as well as vitamin D, calcium, iron, and potassium, are listed on the nutrition facts label on packaged food (US Food & Drug Administration, 2022). Noteworthily, fatty acids could be determined with the official methods of analysis published by AOAC International (Latimer, 2019), and the results are ultimately listed as the total value on the nutrition facts label. According to the AOAC official method 972.28 (first adopted in 1972 and revised in 1985), total fat is calculated after continuous extraction with hexane. In contrast, total fat, total saturated fatty acid, and total monounsaturated fatty acid are calculated as sum of respective fatty acids from a limited list that are determined by GC after methyl esterification based on AOAC official method 996.06 (first adopted in 1996 and revised in 2001). It can be observed that there has been a transition from measuring the content of total fatty acid to individual fatty acids to control food quality more precisely. However, the current method still has some shortcomings. One shortcoming of these methods is making scientists and food manufacturers focus on high-content phytochemicals. The other is that various fatty acids or their derivatives and other bioactive compounds are neglected even with high activities. Given that the contributions of these active compounds especially low-level ones to food quality are ignored using current QC methods, more sensitive and precise QC methods are warranted to provide better coverage of bioactive nutrients and food functions and avoid the limitation of level. Accordingly, the aim of this commentary is to introduce two new food QC concepts, bioactive equivalency factor (BEF) and bioactive equivalency (BEQ), to assess food quality precisely. In the big-data era, scientists have established many databases to record or predict active compounds, such as BIOPEP-UWM database of bioactive peptides (Minkiewicz et al., 2019) and AOD: the antioxidant protein database (Feng et al., 2017). Meanwhile, bioactivities of compounds can be predicted with quantitative structure–activity relationships (QSARs) modeling that uses a wide variety of statistical and machine learning techniques (Cherkasov et al., 2014). Therefore, combined with bioassays (Xiao et al., 2020), it is easy to obtain the activity of individual compounds. With the content of compounds determined, an attempt can be made to establish a new standard to precisely assess the contributions of individual compounds to food quality. Thus, two new concepts, BEQ and BEF, were proposed to evaluate food quality considering both compound levels and bioactivities. According to Equation (1), BEQ is influenced by both the number and content of nutrients in food and thus can meet the needs of assessing food quality more precisely. According to Equation (2), the BEF of a specific compound is inversely proportional to IC50 i. Noteworthy, the product of an insignificant number multiplied by an extremely large one will be sizable. Consequently, a lower IC50 would generate a larger BEQ in Equation (3), compounds even with low levels and those with low IC50s should be emphasized, following the “less is more” hypothesis. After defining and deducing BEQ theoretically, can BEQ be put into action? There are two different situations. First, tens of thousands of studies on individual nutritional compounds have indicated the feasibility of calculating BEF. It was reported that the IC50 of gallic acid, VC, and caffeic acid is 0.67, 2.28, and 1.96 μg/ml, respectively (Spagnol et al., 2019); the IC50 of chlorogenic acid and VC is 40.8 and 11.2 μg/ml, respectively (Hwang et al., 2018). Meanwhile, the ABTS radical scavenging activities of cysteine and VC were 96.77% and 91.78%, respectively, at 100 μg/ml, whereas methionine did not exhibit any such activity (Kim et al., 2020). Therefore, the BEFgallic acid = 2.28/0.67 = 3.40, BEFcaffeic acid = 2.28/1.96 = 1.16, BEFchlorogenic acid = 11.2/40.8 = 0.27, BEFCysteine = 96.77%/91.78% = 1.05, and BEFMethionine = 0. Second, while IC50 or percentage inhibition are preferred for calculating the BEF, not all data can be obtained from the literature. Luckily, the IC50 can be predicted by machine learning approaches, such as the QSAR model (Ahmadi et al., 2021), as well as antibacterial (Bouarab-Chibane et al., 2019) and antihypertensive (Wang et al., 2020) capacities. Of note, BEQ is also applicable to other bioactivities: when the activity (e.g., antioxidant activity) is linearly proportional to concentration within a range, the set of BEF is the same as above mentioned; when extending the BEQ to other functions, the calculation of BEFs should consider the proper dose-effect relationships. With BEF of bioactive food chemicals estimated and chemical levels determined, it would be feasible to calculate BEQ according to Equation (1), and then to assess food quality with further precision. For example, if all the mentioned compounds except VC were detected in food 1 and 2, C1 gallic acid = 0.5, C1 caffeic acid = 0.8, C1 chlorogenic acid = 2.0, C1 cysteine = 0.9, C1 methiLonine = 2.5, C2 gallic acid = 0.8, C2 caffeic acid = 1.3, C2 chlorogenic acid = 0.9, C2 cysteine = 1.2, C2 methionine = 1.0 μg/100 mg dry weight. Obviously, sum1 AAs (=3.4) > sum2 AAs (=2.2), sum1 PAs (=3.3) > sum2 PAs (=3.0), sum1 total (=6.7) > sum2 total (=5.2). It is thus easy to conclude that food 1 is “better” than food 2 by comparing their contents. However, BEQ1 (=4.1) < BEQ2 (=5.7) using Equation (1) reveals that food 2 is a better antioxidant dietary source. From this theoretical example of BEQ calculation, there are two interesting findings. Low levels of highly active compounds influence the total content of their class a little, which will produce a pseudo-low result. Albeit, with the concept of BEQ, such result is reversed, which is the real quality of the food clarifying the necessity of normalization with BEQ for food quality assessment. The general workflow of food quality evaluation using BEQ is shown in Figure 1. Bioactive compounds in food are essential to life or have health benefits to human body. However, current food QC methods cannot precisely reflect the contribution of individual functional compounds to food quality. Fortunately, the application of BEQ makes it feasible to assess food quality precisely, although more discussion and scientific data, especially from studies in vivo, are needed for the calculation of BEF. We call for more attention toward individual compounds in food especially low-content but highly active ones and hope that in the future, BEF will be officially set by the national government, and BEQ will be included on food nutrition facts labels for consumers. In conclusion, with the emphasis on the “less is more” concept and application of BEQ, we are closer to improving food quality that considers both quality and quantity. This work was supported by the Science and Technology Development Fund, Macau SAR [grant number FDCT 0025/2021/A1]. The authors declare no conflict of interest.