Galera Leonardo de Aro, Abdalla Filho Adibe Luiz, Reis Luiza Santos, de Souza Janaina Leite, Hernandez Yeleine Almoza, Martinelli Luiz Antonio
Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil.
PeerJ. 2019 Feb 20;7:e5828. doi: 10.7717/peerj.5828. eCollection 2019.
Brazil is a low- to medium-income country and has the second largest pet food market in the world with 8% of world pet food consumption. The lowest-income social class spends around 17% of their domestic budget on pet food and other items related to pets. Consumers are frequently misled by advertising as there is no precise information about the main sources of protein, carbohydrates and fat in the labels, and the Brazilian pet food industry can legally claim that their products contain certain items like salmon or beef even if they use just a flavoring compound.
The stable isotope methodology compares the stable isotope ratios of carbon (C/C) and nitrogen (N/N) between source and product. The C/C ratio of a specific product (e.g., dog food) reveals the proportions of C (maize) and C (soybean, rice and wheat) plants in that product and the N/N ratio reveals the proportion of the compounds derived from animals. With this isotopic data, we used MixSIAR, a Bayesian stable isotope-mixing model, to estimate the proportion of maize, grains, poultry and beef in dog food.
The δC values of dry dog food ranged from -24.2‰ to -12.8‰, with an average (± standard-deviation) of -17.1‰ ± 2.8‰. The δC values of wet pet food ranged from -25.4‰ to -16.9‰, with an average (± standard-deviation) of -21.2‰ ± 2.4‰, which was significantly lower ( < 0.01). The δN values of the dry and wet food ranged from 1.7‰ to 4.2‰, and from 0.5‰ to 5.5‰, respectively. The average δN values of dry food (2.9‰ ± 0.5‰) was not higher than the wet food (2.6‰ ± 1.3‰) ( > 0.01). The output of the MixSIAR showed a low proportion of bovine products in dry dog food samples. On the other hand, poultry was obviously the dominant ingredient present in most of the samples. Maize was the second dominant ingredient. Wet and dry dog food showed similar isotopic analysis results. The only difference was a lower proportion of maize and higher proportion of grains in wet dog food.
The main finding is that dog food in Brazil is mostly made of approximately 60% (ranging from 32% to 86%) animal-based and 40% (ranging from 14% to 67%) plant-based products. Poultry and maize are the main ingredients. Poultry is added as a by-product or meal, which avoids competition between dogs and humans for meat products, while they can compete for maize. On the other hand, a large proportion of plant-based products in dog food decreases the energy and environmental footprint, since plant-based food products tend to be less harmful compared to animal-based products. Labels can mislead consumers by showing pictures of items that are not necessarily part of the product composition and by not showing the detailed information on the proportion of each ingredient. This information would allow customers to make their own choices considering their pet's nutrition, the competition between animals and humans for resources and environmental sustainability.
巴西是一个中低收入国家,拥有世界第二大宠物食品市场,占全球宠物食品消费量的8%。收入最低的社会阶层将其家庭预算的约17%用于宠物食品和其他与宠物相关的物品。消费者经常受到广告的误导,因为标签上没有关于蛋白质、碳水化合物和脂肪主要来源的精确信息,而且巴西宠物食品行业可以合法宣称其产品含有某些物品,如三文鱼或牛肉,即使它们只使用了一种调味化合物。
稳定同位素方法比较了来源和产品之间碳(C/C)和氮(N/N)的稳定同位素比率。特定产品(如狗粮)的C/C比率揭示了该产品中C(玉米)和C(大豆、大米和小麦)植物的比例,而N/N比率揭示了来自动物的化合物的比例。利用这些同位素数据,我们使用了MixSIAR,一种贝叶斯稳定同位素混合模型,来估计狗粮中玉米、谷物、家禽和牛肉的比例。
干狗粮的δC值范围为-24.2‰至-12.8‰,平均(±标准差)为-17.1‰±2.8‰。湿宠物食品的δC值范围为-25.4‰至-16.9‰,平均(±标准差)为-21.2‰±2.4‰,显著更低(<0.01)。干、湿食品的δN值分别范围为1.7‰至4.2‰和0.5‰至5.5‰。干食品的平均δN值(2.9‰±0.5‰)不高于湿食品(2.6‰±1.3‰)(>0.01)。MixSIAR的输出显示,干狗粮样本中牛产品的比例较低。另一方面,家禽显然是大多数样本中的主要成分。玉米是第二主要成分。湿狗粮和干狗粮显示出相似的同位素分析结果。唯一的区别是湿狗粮中玉米比例较低,谷物比例较高。
主要发现是,巴西的狗粮大多由约60%(范围为32%至86%)的动物性产品和40%(范围为14%至67%)的植物性产品制成。家禽和玉米是主要成分。家禽作为副产品或肉粉添加,这避免了狗和人类对肉类产品的竞争,而它们会争夺玉米。另一方面,狗粮中很大一部分植物性产品减少了能源和环境足迹,因为与动物性产品相比,植物性食品往往危害较小。标签可能会误导消费者,通过展示不一定是产品成分一部分的物品图片,以及不显示每种成分比例的详细信息。这些信息将使客户能够根据宠物的营养、动物与人类对资源的竞争以及环境可持续性做出自己的选择。