61. 蛋粉之加工製造，去糖或脫糖處理的最主要目的為：
(A)降低蛋液黏度，提高蛋粉之乾燥效率
(B)降低梅納反應，提高蛋粉之色澤品質
(C)降低甜度，提高蛋粉之風味品質
(D)降低結塊反應，提高蛋粉之復水性

62. 紅葡萄酒於發酵過程中，添加偏重亞硫酸鉀的主要目的為： (A)殺死雜菌及穩定色澤 (B)提高鹼度及控制風味 (C)控制凝固沉降及酒石酸、蘋果酸之含量 (D)控制酵母菌的發酵能力 115 年第一次食品品保工程師-初級能力鑑定【公告試題】 第二科：食品科學概論 考試日期：115 年 05 月 23 日 第 12 頁，共 12 頁 答案 題目

63. 柴魚製品之加工會利用到下列何種微生物，以產生柴魚的特殊風味？ (A)醋酸菌 (B)黴菌 (C)酵母菌 (D)乳酸菌

64. 下列何種食品之加工製造，不涉及發酵技術？ (A)普洱茶 (B)梅乾菜 (C)板豆腐 (D)優酪乳

65. 茶葉之發酵主要係靠： (A)酵母菌 (B)乳酸菌 (C)自身酵素 (D)環境中存在的微生物

66. 低酸性食品的定義是指食品的酸鹼值要在多少以上？ (A)7.0 (B)6.0 (C)4.6 (D)3.5

1. Jack use OLS method to estimate the parameters of linear model with intercept: $y_t = \alpha + \beta x_t + \varepsilon_t.$ If the heteroskedasticity ($Var(\varepsilon_t | x_t) = \sigma^2_x x_t$) exits in the errors but other Gauss-Markov conditions satisfy, which statement is true?      (1)      (A). The estimated parameter, \( \hat{\beta} \), is biased, and variance of \( \hat{\beta}_1 \) obtained by OLS method is incorrect. (B). The estimated parameter, \( \hat{\beta} \), is biased, and variance of \( \hat{\beta}_1 \) obtained by OLS method is accurate. (C). The estimated parameter, \( \hat{\beta} \), is unbiased, and variance of \( \hat{\beta}_1 \) obtained by White heteroskedasticity-robust method is accurate. (D). The estimated parameter, \( \hat{\beta}_1 \), is unbiased and efficiency. (E). Jack cannot use OLS to estimate parameters because of inefficiency.

2. Consider the simple linear regression model: $y_t = \beta y_{t-1} + \varepsilon_t,$with the error structure as below: $\varepsilon_t = \rho \varepsilon_{t-1} + u_t,$ where $u_t \sim i.i.d(0, \sigma^2)$. What is the probability limit ($\rho$ lim) of OLS estimator $\hat{\beta}$?      (2)      (A). \( \frac{\beta}{1 + \beta\rho} \) (B). \( \beta \) (C). \( \frac{\beta + \rho}{1 + \beta\rho} \) (D). \( \frac{\rho}{1 + \beta\rho} \) (E). \( \frac{1}{1 + \beta\rho} \)

3. Consider the model: $y_t = \alpha + \rho x_t + \varepsilon_t,$ where $t = 1, \dots, T$. If we want to obtain consistent estimator $\hat{\rho}$ for the large sample, which necessary condition it must be have?      (3)      (A). \( \operatorname{cov}(\varepsilon_t x_t) = 0 \) (B). \( Var(\varepsilon_t \mid 1, x_t) = \sigma^2 \) (C). \( E(\varepsilon_t \mid 1, x_t) = 0 \) (D). \( E(\varepsilon_t \mid x_t) = 0 \) (E). \( E(\varepsilon_t\varepsilon_s|x_t,x_s) = 0 \)

4. An economist uses data to estimate the linear model with intercept $y_t = \alpha + \beta_1 x_{1t} + \varepsilon_t,$ but the true population model is as below: $y_t = \alpha + \beta_1 x_{1t} + \beta_2 x_{2t} + u_t.$ Under which condition satisfied, the OLS estimator $\hat{\beta}_1$ is still consistent?      (4)      (A). \( E(x_{2t}u_t) = 0 \) (B). \( \alpha = 0 \) (C). \( E(x_{2t}\varepsilon_t) = 0 \) (D). \( E(x_{2t}|x_{1t}) = 0 \) (E). \( E(x_{1t}u_t) = 0 \)

5. Suppose that there is an exact relationship between two scalar random variable y and x: $y = \beta x + v$However, $y$ is not known and unobserved. A variable $x^*$ is observed, such that $x^* = x + u$ Under which condition satisfied, the OLS estimator $\hat{\beta}_1$ is still consistent?      (5)      (A). \( E(y\alpha^{*}) = 0 \) (B). \( E(x^{*}u) = 0 \) (C). \( E(xu) = 0 \) (D). \( E(xv) = 0 \) (E). \( E(uv) = 0 \)

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